Mathematical modeling of the effects of atmospheric pressure on mosquito (Diptera: Culicidae) population density in Villa Clara, Cuba
Modelación matemática del efecto de la presión atmosférica sobre la densidad poblacional de los mosquitos (Diptera: Culicidae) en Villa Clara, Cuba
DOI:
https://doi.org/10.15446/revfacmed.v68n4.79516Palabras clave:
Cuba, Culicidae, Forecasting, Disease Vectors (en)Cuba, Culicidae, Predicción, Vectores de enfermedades (es)
Descargas
Introduction: Mosquitoes (Diptera: Culicidae) are considered one of the most versatile organisms in the world as they can breed in any pool of water, such as puddles or tanks. However, their reproduction is influenced by atmospheric variables that allow predicting their population density.
Objective: To assess the impact of atmospheric pressure on mosquito population density in the province of Villa Clara, Cuba, by means of a mathematical model based on a regressive objective regression (ROR) methodology.
Materials and methods: The development of the mathematical model to predict breeding sites was based on the number of breeding sites reported in the province of Villa Clara between 2000 and 2017, and the ROR model. Furthermore, a regression analysis was carried out using the IBM SPSS® version 19.0, which allowed obtaining models that explained 100% of the variance, with its corresponding standard error.
Results: With respect to the number of breeding sites, an increasing trend was observed in the municipality of Cifuentes, while the trend was downward in Ranchuelo and Caibarién. The municipalities of Santa Clara and Encrucijada had the highest and lowest standard deviation (13.432 vs. 5.968, respectively), which demonstrates great variability among the data of each municipality.
Conclusions: There is a close relationship between atmospheric pressure and mosquito population density since both total and specific larval densities increase as atmospheric pressure increases.
Introducción. Los mosquitos (Diptera: Culicidae) son uno de los organismos más versátiles del mundo, pues pueden reproducirse en cualquier depósito de agua, como charcos o tanques. Sin embargo, su reproducción está influenciada por variables atmosféricas que permiten predecir su densidad poblacional.
Objetivo. Evaluar el impacto de la presión atmosférica en la densidad poblacional de mosquitos en la provincia de Villa Clara, Cuba, mediante el uso de modelos matemáticos basados en la metodología de regresión objetiva regresiva (ROR).
Materiales y métodos. El desarrollo del modelo matemático de pronóstico de focos de reproducción se basó en el número de focos reportados en la provincia de Villa Clara entre 2000 y 2017, y en el modelo ROR. Además, se realizó un análisis de regresión mediante el programa IBM SPSS® versión 19.0, lo que permitió obtener modelos de regresión que explicaron el 100% de la varianza, con su error típico.
Resultados. Respecto a la cantidad de focos, se observó una tendencia al aumento en el municipio de Cifuentes, mientras que en Ranchuelo y Caibarién la tendencia fue a la reducción. Los municipios de Santa Clara y Encrucijada tuvieron la desviación estándar más alta y más baja, respectivamente (134.32 vs. 5.968), lo que evidencia una gran variabilidad entre los datos de cada municipio.
Conclusiones. Existe una estrecha relación entre la presión atmosférica y la densidad poblacional de mosquitos, ya que a medida que aumenta la presión atmosférica, aumentan las densidades larvales, tanto total como específicas.

ORIGINAL RESEARCH
DOI: https://doi.org/10.15446/revfacmed.v68n4.79516
Received: 06/05/2019. Accepted: 31/07/2019
Mathematical modeling of the effects of atmospheric pressure on mosquito (Diptera: Culicidae) population density in Villa Clara, Cuba
Modelación matemática del efecto de la presión atmosférica sobre la densidad poblacional de los mosquitos (Diptera: Culicidae) en Villa Clara, Cuba
Rigoberto Fimia-Duarte1 Ricardo Osés-Rodríguez2
Pedro María Alarcón-Elbal3
Jaime Wilfrido Aldaz-Cárdenas4
Bárbara Verónica Roig-Boffill5 Pedro Yoelvys de la Fé-Rodríguez5
1 Universidad de Ciencias Médicas de Villa Clara - School of Health Technology and Nursing - Santa Clara - Cuba.
2 Centro Meteorológico Provincial de Villa Clara-Santa Clara - Villa Clara - Cuba.
3 Universidad Iberoamericana (UNIBE) -Institute for Tropical Medicine & Global Health (IMTSAG) - Santo Domingo - Dominican Republic.
4 Universidad Estatal de Bolívar - Faculty of Agricultural Sciences, Natural Resources and Environment - School of Veterinary Medicine and Zootechnics - Provincia Bolívar - Ecuador.
5 Universidad Central «Marta Abreu» de Las Villas - Faculty of Agricultural Sciences - Undergraduate program in Veterinary Medicine and Zootechnics - Villa Clara - Cuba.
Corresponding author: Rigoberto Fimia-Duarte. Facultad de Tecnología de la Salud y Enfermería, Universidad de Ciencias Médicas de Villa Clara. Carretera Sakenaf km 1 e/ Circunvalación y Reparto Sakenaf, Departamento de Posgrado e Investigación. Telephone number: +53 42 222599. Santa Clara. Cuba. Email: rigoberto.fimia66@gmail.com.
Abstract
Introduction: Mosquitoes (Diptera: Culicidae) are considered one of the most versatile organisms in the world as they can breed in any pool of water, such as puddles or tanks. However, their reproduction is influenced by atmospheric variables that allow predicting their population density.
Objective: To assess the impact of atmospheric pressure on mosquito population density in the province of Villa Clara, Cuba, by means of a mathematical model based on a regressive objective regression (ROR) methodology.
Materials and methods: The development of the mathematical model to predict breeding sites was based on the number of breeding sites reported in the province of Villa Clara between 2000 and 2017, and the ROR model. Furthermore, a regression analysis was carried out using the IBM SPSS® version 19.0, which allowed obtaining models that explained 100% of the variance, with its corresponding standard error.
Results: With respect to the number of breeding sites, an increasing trend was observed in the municipality of Cifuentes, while the trend was downward in Ranchuelo and Caibarién. The municipalities of Santa Clara and Encrucijada had the highest and lowest standard deviation (13.432 vs. 5.968, respectively), which demonstrates great variability among the data of each municipality.
Conclusions: There is a close relationship between atmospheric pressure and mosquito population density since both total and specific larval densities increase as atmospheric pressure increases.
Keywords: Cuba; Culicidae; Forecasting; Disease Vectors (MeSH).
Fimia-Duarte R, Osés-Rodríguez R, Alarcón-Elbal PM, Aldaz-Cárdenas JW, Roig-Boffill BV, de la Fé-Rodríguez PY. Mathematical modeling of the effects of atmospheric pressure on mosquito (Diptera: Culicidae) population density in Villa Clara, Cuba. Rev. Fac. Med. 2020;68(4):541-9. English. doi: https://doi.org/10.15446/revfacmed.v68n4.79516.
Resumen
Introducción. Los mosquitos (Diptera: Culicidae) son uno de los organismos más versátiles del mundo, pues pueden reproducirse en cualquier depósito de agua, como charcos o tanques. Sin embargo, su reproducción está influenciada por variables atmosféricas que permiten predecir su densidad poblacional.
Objetivo. Evaluar el impacto de la presión atmosférica en la densidad poblacional de mosquitos en la provincia de Villa Clara, Cuba, mediante el uso de modelos matemáticos basados en la metodología de regresión objetiva regresiva (ROR).
Materiales y métodos. El desarrollo del modelo matemático de pronóstico de focos de reproducción se basó en el número de focos reportados en la provincia de Villa Clara entre 2000 y 2017, y en el modelo ROR. Además, se realizó un análisis de regresión mediante el programa IBM SPSS® versión 19.0, lo que permitió obtener modelos de regresión que explicaron el 100% de la varianza, con su error típico.
Resultados. Respecto a la cantidad de focos, se observó una tendencia al aumento en el municipio de Cifuentes, mientras que en Ranchuelo y Caibarién la tendencia fue a la reducción. Los municipios de Santa Clara y Encrucijada tuvieron la desviación estándar más alta y más baja, respectivamente (134.32 vs. 5.968), lo que evidencia una gran variabilidad entre los datos de cada municipio.
Conclusiones. Existe una estrecha relación entre la presión atmosférica y la densidad poblacional de mosquitos, ya que a medida que aumenta la presión atmosférica, aumentan las densidades larvales, tanto total como específicas.
Palabras clave: Cuba; Culicidae; Predicción; Vectores de enfermedades (DeCS).
Fimia-Duarte R, Osés-Rodríguez R, Alarcón-Elbal PM, Aldaz-Cárdenas JW, Roig-Boffill BV, de la Fé-Rodríguez PY. [Modelación matemática del efecto de la presión atmosférica sobre la densidad poblacional de los mosquitos (Diptera: Culicidae) en Villa Clara, Cuba]. Rev. Fac. Med. 2020;68(4):541-9. English. doi: https://doi.org/10.15446/revfacmed.v68n4.79516.
Introduction
Throughout history, humanity has faced multiple potentially deadly viral and parasitic diseases, including yellow fever, dengue, zika, chikungunya, and malaria, which are closely related because they are transmitted by a mosquito (Diptera: Culicidae).1 These diseases are highly prevalent in tropical areas and the risk of contracting them depends, to a large extent, on rainfall, temperature and the living conditions of each place.2
Mosquitoes are considered one of the most versatile living organisms in the world as they can breed in any pool of water, such as puddles or tanks; however, their reproduction is influenced by certain biotic and abiotic factors.3,4 These insects need the blood of humans and/or other vertebrates to make their eggs fertile and viable, so they represent a threat to humanity since they are vectors of pathogenic microorganisms of several vector-borne diseases that can become burdens for many countries from the health, economic and social point of view.5,6
Natural disasters threaten the safety of people, hurricanes being the most destructive phenomenon in the tropics because of all that their passage entails.7-10 Since the frequency of these events is on the rise, knowing their behavior is essential; for this reason, in Cuba, seasonal forecasts are constantly made based on the oceanic and atmospheric conditions that preceded the past hurricane seasons (active or inactive), as they provide useful information about trends that could be observed in similar future seasons.11
Although the occurrence of hurricanes cannot be predicted accurately, their impact can be determined in advance. Moreover, research in this area has made significant progress in recent years, so much so that in 2012, for the first time, it was possible to forecast an event of this type a year in advance.12 To this end, high-quality forecasts are made using the regressive objective regression (ROR) methodology, which, due to its simplicity and accuracy, can be useful to know the future of climate variables or daily data years in advance.13,14 Forecast times can extend to 11 years of the solar cycle or other longer cycles known in nature.
By using the ROR methodology, the population dynamics of insects such as mosquitoes, and their interactions with certain environmental variables, can also be modeled. This is helpful to establish timely control and prophylactic measures in epidemiological surveillance programs.12,13,15
In this sense, the objective of this research was to assess the impact of atmospheric pressure on mosquito population density in the province of Villa Clara, Cuba, through mathematical models based on the ROR methodology.
Materials and methods
Area and period of study
The study was carried out between 2007 and 2017 in Villa Clara, a province located in the central region of Cuba, bordering on the north with the Atlantic Ocean and on the east, west and south with the provinces of Sancti Spíritus, Matanzas and Cienfuegos, respectively. Politically and administratively speaking, Villa Clara has 13 municipalities, and its capital is Santa Clara (Figure 1).

Figure 1. Political map of Cuba and the province of Villa Clara.
Source: Own elaboration.
Currently, Villa Clara has five weather stations located in Sagua la Grande (latitude 22º48’17’’ N and longitude 80º05’32’’ W), La Piedra (latitude 22º06’48’’ N
and longitude 79º58’48’’ W), Santo Domingo (latitude 22º35’08’’ N and longitude 80º13’35’’ W), Caibarién
(latitude 22º29’48’’ N and longitude 78º28’14’’ W) and El Yabú (latitude 22º27’43’’ N and longitude 79º59’30’’ W),
the latter located in the city of Santa Clara.
Catching and identifying mosquitoes
Trees and shrubs located on the margins of river ecosystems, as well as discarded artificial water-holding containers (cans, bottles, tires, etc.), were inspected to capture mosquitoes in preimaginal stages. Samples were collected in rivers, streams, ditches, gullies, lagoons, marshes and swamps, with special emphasis on banks because they are usually quiet and shallow environments where mosquitoes breed.16 Similarly, in order to compare the results obtained with the information available in the area and establish possible coincidences, the records and maps of the Unidad Provincial de Vigilancia y Lucha Antivectorial (Provincial Unit for Antivectorial Fight and Surveillance - UPVLA) of Villa Clara were consulted, as it archives the entomological records of the region.
Larvae and pupae were captured using ladles17 and transferred to the laboratory in 250mL plastic bottles duly labeled; general larval density was considered for this process. On the other hand, adult mosquitoes were captured using light traps and were transported in 50 mL glass jars, which contained absorbent cotton balls impregnated with ether and/or chloroform.
Preimaginal and adult specimens captured were identified in the Medical Entomology Laboratory of the UPVLA according to dichotomous and pictorial access keys18-20 and following the taxonomic classification of the Systematic Catalog of Culicidae of the Walter Reed Biosystematics Unit.21 As part of the research, the annual number (2007-2017) of breeding sites per municipality in the province was considered and the impact of atmospheric pressure on mosquito population in the Santo Domingo station was analyzed since the results can be extrapolated to the rest of the municipalities in the province due to their geographical proximity.
Statistical analysis
The ROR methodology, which consists of several steps, was used to develop the mathematical model to predict breeding sites.22,23 First, sawtooth (S) and inverted sawtooth (IS) dichotomous variables were created for the number of cases (NoC) in the database used for ROR modeling. The NoC coefficient in the model represented the trend of the series and the variable to model was the number of mosquito breeding sites. Thus, the variability of the number of breeding sites was established by S and IS coefficients plus the NoC coefficient, explaining a large percentage of the variance (close to 100%). The S value was 1 for odd NoC, the IS value was 0 for even NoC, and vice versa.
Later, a regression analysis was performed using the statistical package SPSS version 19.0, specifically with the ENTER method, which allowed obtaining the predicted variable and the ERROR.
Then, the autocorrelogram of the variable ERROR was obtained taking into account the maximum values of the partial autocorrelation function (PACF). New variables were then calculated based on the significant lag of the PACF and were included in a new multivariate linear regression analysis, where they were regressed in a successive approximation process until a white noise was obtained in the regression errors. In the case of the atmospheric pressure, one-year lags were used, as done by other authors for climatic indexes.10,22,23 It should be noted that, in the Provincial Meteorological Center of Villa Clara, the atmospheric pressure values are recorded every three hours.
For mathematical modeling, information on the number of breeding sites reported annually in each of the municipalities of Villa Clara between 2007 and 2017 was taken into account; these data were analyzed through descriptive statistics: the minimum and maximum values were established and the average was estimated.
Results
In the present research work, 43 species of mosquitoes of 13 genera were identified in Villa Clara. Several of these species had marked ubiquity (Table 1).
Table 1. Distribution of the identified mosquito species in the province of Villa Clara, Cuba. 2007-2017.
Mosquito species |
Classification and year |
Municipalities * |
Total number of municipalities |
Aedeomyia squamipennis |
Lynch Arribálzaga, 1878 |
9, 12 |
2 |
Anopheles albimanus |
Wiedemann, 1821 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Anopheles atropos |
Dyar & Knab, 1906 |
5, 6 |
2 |
Anopheles grabhamii |
Theobald, 1901 |
5 , 6 , 11 |
3 |
Anopheles vestitipennis |
Dyar & Knab, 1906 |
3, 5, 6, 7, 8, 9, 11 |
7 |
Anopheles crucians |
Wiedemann, 1828 |
5 , 8 , 12 |
3 |
Aedes aegypti |
Linnaeus, 1762 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Aedes albopictus |
Skuse, 1894 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Aedes mediovittatus |
Coquillett, 1906 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Aedes scapularis |
Rondani, 1848 |
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
12 |
Aedes sollicitans |
Walker, 1856 |
1, 3, 4, 5, 6, 7, 10, 11 |
8 |
Aedes taeniorhynchus |
Wiedemann, 1821 |
1, 2, 3, 4, 5, 6, 7, 10, 11 |
9 |
Aedes tortilis |
Theobald, 1903 |
3, 4, 5, 7, 9 |
5 |
Aedes walkeri |
Theobald, 1901 |
2, 6, 11, 12 |
4 |
Coquillettidia nigricans |
Coquillett, 1904 |
9, 11 |
2 |
Culex americanus |
Neveu-Lemaire, 1902 |
6, 9 |
2 |
Culex atratus |
Theobald, 1901 |
4, 5, 6, 8, 9, 10 |
6 |
Culex bahamensis |
Dyar & Knab, 1906 |
6, 8 |
2 |
Culex cancer |
Theobald, 1901 |
1, 5 , 6 |
3 |
Cx. chidesteri |
Dyar, 1921 |
1, 2, 6, 8, 9, 11, 12 |
7 |
Culex corniger |
Theobald, 1903 |
2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 13 |
12 |
Culex erraticus |
Dyar & Knab, 1906 |
4, 5, 6, 7, 8, 9, 10, 12, 13 |
9 |
Culex iolambdis |
Dyar, 1918 |
8, 9 |
2 |
Culex nigripalpus |
Theobald, 1901 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Culex pilosus |
Dyar & Knab, 1906 |
1, 3, 4, 5, 6, 8, 13 |
7 |
Culex quinquefasciatus |
Say, 1823 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Culex sphinx |
Howard, Dyar & Knab, 1915 |
6 |
1 |
Culex secutor |
Theobald, 1901 |
8, 13 |
2 |
Mansonia titillans |
Walker, 1848 |
3, 6, 8, 9, 10, 11, 12 |
7 |
Limatus durhamii |
Theobald, 1901 |
9, 12 |
2 |
Orthopodomyia signifera |
Coquillett, 1896 |
8, 12 |
2 |
Psorophora ciliata |
Fabricius, 1794 |
1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13 |
12 |
Psorophora confinnis |
Lynch Arribálzaga, 1891 |
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 |
13 |
Psorophora howardii |
Coquillett, 1901 |
6, 7, 8, 9, 10, 12, 13 |
7 |
Psorophora johnstonii |
Grabham, 1905 |
6 |
1 |
Psorophora pygmaea |
Theobald, 1903 |
1, 4, 5, 6, 7, 9, 10, 13 |
8 |
Psorophora santamarinai |
González Broche, 2000 |
6 |
1 |
Psorophora insularia |
Dyar and Knab, 1906 |
6 |
1 |
Psorophora infinis |
Dyar and Knab, 1906 |
8 |
1 |
Toxorhynchites portoricensis |
von Röder, 1885 |
5, 6 |
2 |
Uranotaenia sapphirina |
Osten-Sacken, 1868 |
3, 4, 5, 6, 7, 8, 9, 11, 12 |
9 |
Wyeomyia vanduzeei |
Dyar & Knab, 1906 |
9 |
1 |
Wyeomyia mitchellii |
Theobald, 1905 |
8, 9 |
2 |
* Each municipality was identified with a number: 1: Corralillo, 2: Quemado de Güines, 3: Sagua la Grande, 4: Encrucijada, 5: Camajuaní, 6: Caibarién, 7: Remedios, 8: Placetas, 9: Santa Clara, 10: Cifuentes, 11: Santo Domingo, 12: Ranchuelo, and 13: Manicaragua.
Source: Own elaboration.
Table 2 presents the descriptive statistics for the number of breeding sites per municipality; it is possible to see that the highest mean and standard deviation (σ) are found in the municipality of Santa Clara and that the lowest are in Encrucijada. The maximum value obtained for the time series (11 years of annual data) was 8 778 in Santa Clara, while the minimum value was 0 in Corralillo, Quemado de Güines, Encrucijada and Caibarién. The overall average number of breeding sites in the province was 7 143, with a σ of 2 165.
Table 2. Descriptive statistics of the number of annual breeding sites in the province of Villa Clara, Cuba 2007-2017.
Municipalities |
N |
Minimum |
Maximum |
Mean |
σ |
Corralillo |
11 |
0.00 |
44.00 |
12.72 |
15.49 |
Quemado de Güines |
11 |
0.00 |
117.00 |
17.00 |
34.33 |
Sagua la Grande |
11 |
4.00 |
1 199.00 |
475.27 |
468.07 |
Encrucijada |
11 |
0.00 |
14.00 |
6.36 |
5.35 |
Camajuaní |
11 |
1.00 |
108.00 |
44.36 |
36.37 |
Caibarién |
11 |
0.00 |
54.00 |
18.81 |
18.55 |
Remedios |
11 |
1.00 |
249.00 |
52.00 |
76.57 |
Placetas |
11 |
10.00 |
438.00 |
89.45 |
126.31 |
Santa Clara |
11 |
3 734.00 |
8 778.00 |
5 987.63 |
1 549.21 |
Cifuentes |
11 |
3.00 |
78.00 |
25.72 |
25.29 |
Santo Domingo |
11 |
5.00 |
204.00 |
91.09 |
80.06 |
Ranchuelo |
11 |
36.00 |
675.00 |
185.81 |
214.27 |
Manicaragua |
11 |
6.00 |
381.00 |
137.09 |
145.17 |
Provincia |
11 |
4 497.00 |
10 806.00 |
7 143.36 |
2 165.59 |
σ: standard deviation; N: number of years studied from 2007 to 2017.
Source: Own elaboration.
The ROR model was chosen because, during the investigation, it was concluded that by combining lags 1, 2 and 6 (corresponding to 1 year, 2 years and 6 years, respectively) a perfect model could be obtained for each municipality but the parameters of influence could not be measured since there were divisions by zero. The proposed model proved to be perfect according to the analyzed data since no standard error was observed and no autocorrelation was detected in the residues by means of the Durbin Watson statistic. The analysis of variance (ANOVA) for the model indicated that Fisher’s F could not be determined because the error was zero, and to calculate this statistic, it is necessary to divide by the error, which creates a mathematical uncertainty.
Figure 2 shows the result of the modeling for the province; it is possible to see that the predicted value has excellent coincidence with the real value from 2013 to 2017.

Figure 2. Real and predicted value of the number of mosquito breeding sites in the province of Villa Clara, Cuba.
Source: Own elaboration.
The highest variance explained was the same for all municipalities (100%), but there was an upward trend in Cifuentes since the NoC coefficient was positive, while the trend was downward in Caibarién and Ranchuelo since the NoC coefficient was negative (Table 3). NoC coefficient was recorded when it was not significant. The ANOVA was only done for the larval stage.
The forecast values obtained using the atmospheric pressure prediction in the Santo Domingo station showed a behavior similar to that calculated without it (Figure 3).
Table 3. Number of breeding sites per municipality for larval density in the province of Villa Clara, Cuba 2007-2017.
Municipality and significance of larval density |
Durbin Watson |
S |
IS |
NoC |
Lag2 * |
Lag1 * |
Lag6 * |
Corralillo 0.002 |
1.459 |
16.99 |
2.709 |
-1.09 |
1.936 |
-2.198 |
|
Quemado de Güines 0.001 |
1.889 |
22.85 |
3 |
0.148 |
-3.889 |
18.407 |
|
Sagua la Grande 0.003 |
0.892 |
2 400 |
3 919 |
-2.359 |
-1.363 |
6.348 |
|
Encrucijada 0.004 |
2.184 |
5.968 |
6.437 |
-0.482 |
0.378 |
-0.194 |
|
Camajuaní 0.023 |
1.655 |
141.74 |
65.98 |
-1.996 |
0.802 |
0.534 |
|
Caibarién 0.014 |
2.044 |
-14.877 |
-4.604 |
3.378 |
-1.375 |
5.901 |
|
Remedios 0.025 |
1.328 |
160.44 |
300.25 |
-2.887 |
-0,601 |
6.846 |
|
Placetas 0.004 |
1.808 |
126.88 |
-14.28 |
-2.776 |
-1.447 |
12.969 |
|
Santa Clara 0.005 |
1.397 |
13 432 |
12 753 |
-0.431 |
-0.755 |
0.008 |
|
Cifuentes 0.032 |
1.567 |
143.35 |
-136.42 |
19.708 |
0.807 |
-0.986 |
|
Santo Domingo 0.002 |
1.570 |
356.31 |
399.82 |
-0.751 |
-1.998 |
15.040 |
|
Ranchuelo 0.004 |
1.630 |
-78.03 |
-130.33 |
-33.253 |
0.253 |
10.685 |
|
Manicaragua 0.004 |
2.415 |
418.46 |
467.47 |
-1.296 |
-0.151 |
0.482 |
|
Province |
1.268 |
14 114 |
12 952 |
-0.546 |
-0.886 |
0.875 |
S: sawtooth; IS: inverted sawtooth; NoC: number of cases.
* Lag 1, Lag 2, and Lag 6 are the predicted lags, which in this case are 1 year, 2 years and 6 years.
Source: Own elaboration.

Figure 3. Real and predicted value of the amount of mosquitoes’ breeding sites in the entire province of Villa Clara, Cuba, using only the predicted value of the atmospheric pressure at sea level in the Santo Domingo station.
Source: Own elaboration.
Discussion
From the 70 species of mosquitoes identified in Cuba so far,16,24 43 were found in Villa Clara in the present study.
The most common genera with the highest presence in the municipalities studied were Anopheles, Culex and Psorophora, while the most common and widely distributed species were Anopheles albimanus, Culex quinquefasciatus, Culex nigripalpus, Aedes mediovittatus, Psorophora confinnis, Aedes aegypti and Aedes albopictus (found in the 13 municipalities), followed by Culex corniger, Aedes scapularis and Psorophora ciliata (found in 12 of the 13 municipalities). The most common species were An. albimanus, Anopheles crucians, Culex atratus, Cx. quinquefasciatus, Cx. nigripalpus and Ps. Confinnis, which were found in practically all the ecosystems sampled and in relatively high abundance, a fact that coincides with what was reported by Marquetti-Fernández,25 specifically for Cx. quinquefasciatus in urban ecosystems. The great phenotypic plasticity of the mosquitoes was also evident since it was found that they are capable of exploiting any habitat.16,26-30
Ae. aegypti and Ae. albopictus are largely found in Villa Clara, which is noteworthy because they are high-risk entomoepidemiological species due to their involvement in infectious diseases such as dengue fever, yellow fever, West Nile fever, chikungunya and Zika virus.31-34 Currently, these two species are present practically in the entire Cuban territory and they are increasingly expanding because human activity generates an important number of breeding sites.33 This expansion shows their high phenotypical plasticity and great capacity of adaptation to different ecological niches.35-37
According to the descriptive statistics, there is great variability between municipalities regarding the number of breeding sites per municipality (Table 2), which may be explained by the physical and geographical characteristics of each place, as well as the quality of the samples, aspects that are consistent with the results of previous research.30,31
Considering that the value obtained in the present study using the Fisher’s F test was high, the model designed here to establish mosquito population density is extremely good since the higher the Fisher’s F, the higher the probability of having a good model in the ANOVA. 38,39
During the research, influence parameters could not be measured because they were divided by zero, so it was necessary to resort to mathematical definitions made by other authors and even for other organisms.40,41 The greatest variance explained was the same for all municipalities and it was 100%. Moreover, as mentioned above, there was a trend towards a decrease in the number of breeding sites in Caibarien and Ranchuelo, while there was an upward trend in Cifuentes. The most significant predicted lags were at 1 year, 2 years and 6 years (Table 3), which coincides with other studies where similar results were obtained and good modeling results were achieved through ROR models.42-44
The impact of the atmospheric pressure predicted in the four stations using the ROR methodology explains 100% of the variance in this new model. From all the pressure measurements per station obtained, the model selected the atmospheric pressure registered in the Santo Domingo station, which resulted in an impact coefficient of 13.84. Therefore, as atmospheric pressure increased in Santo Domingo, the number of breeding sites in the provinces increased; this coincides with previous works carried out in other municipalities of the province to measure larval density9,12,40 and confirms the impact of atmospheric pressure and, consequently, the performance of the North Atlantic anticyclone, which is the main climate modulator in the island of Cuba.
Conclusions
There is a close relationship between atmospheric pressure and mosquito population density since both total and specific larval densities increase as atmospheric pressure increases. These findings confirm the possibility of mathematically modeling the effect of atmospheric pressure on mosquito population density (Diptera: Culicidae) in Villa Clara, Cuba.
Conflicts of interest
None stated by the authors.
Funding
None stated by the authors.
Acknowledgments
None stated by the authors.
References
1.Troyo A, Calderón-Arguedas O, Fuller DO, Solano ME, Avedaño A, Arheart KL, et al. Seasonal profiles of Aedes aegypti (Diptera: Culicidae) larval habitats in an urban area of Costa Rica with a history of mosquito control. J Vector Ecol. 2008;33(1):76-88. http://doi.org/fjfw4j.
2.Organización Mundial de la Salud (OMS). Dengue y dengue grave. Nota descriptiva. Ginebra: OMS; 2019 [cited 2020 Aug 3]. Available from: https://bit.ly/3gt8w6H.
3.Kleinman S, Glynn SA, Busch M, Todd D, Powell L, Pietrelli L, et al. The 2003 West Nile virus UnitedState epidemic: the America’s Blood Centers experience. Transfusion. 2005;45(4):469-79. http://doi.org/d64wtj.
4.Nutt C, Adams P. Zika in Africa-the invisible epidemic? Lancet. 2017;389(10079):1595-6. http://doi.org/ggx92m.
5.Wasserman S, Tambyah PA, Lim PL. Yellow fever cases in Asia: primed for an epidemic. Int J Infect Dis. 2016;48:98-103. http://doi.org/f8sdvk.
6.Weaver SC, Forrester NL. Chikungunya: Evolutionary history and recent epidemic spread. Antiviral Res.2015;120:32-9. http://doi.org/gf2kr5.
7.Adu-Prah S, Kofi-Tetteh E. Spatiotemporal analysis of climate variability impacts on malaria prevalence in Ghana. Appl Geogr. 2015;60:266-73. http://doi.org/f7gjh9.
8.Chaves LF, Morrison AC, Kitron UD, Scott TW. Nonlinear impacts of climatic variability on the density-dependent regulation of an insect vector of disease. Glob Chan Biol. 2012;18(2):457-68. http://doi.org/c9chgq.
9.Osés-Rodríguez R, Fimia-Duarte R, Pedraza-Martínez FA, Saura-González G, Otero-Martín M, Jiménez-Lorenzo F. Modelación de la densidad larvaria total de mosquitos (Diptera: Culicidae) utilizando tres modelos en la provincia de Villa Clara, Cuba. REDVET Rev. Electrón. vet. 2014 [cited 2020 Aug 3];15(8). Available from: https://bit.ly/2Dj3rQ7.
10.Osés-Rodríguez R, Aldaz-Cárdenas JW, Fimia-Duarte R, Segura-Ochoa JJ, Aldaz-Cárdenas NG, Segura-Ochoa JJ, et al. The ROR’s methodology an it’s possibility to find information in a white noise. International Journal of Current Research. 2017;9(3):47378-82.
11.Ortiz-Bultó PL, Pérez-Rodríguez A, Rivero-Valencia A, León-Vega N, Díaz-González M, Pérez-Carrera A. Assessment of human health vulnerability to climate variability and change in Cuba. Environ Health Perspect. 2006;114(12):1942-9.
12.Osés-Rodríguez R,Otero-Martin M, Ruiz Cabrera N, Fimia-Duarte R, Iannacone J. Pronóstico para el huracán Irma por medio de la Regresión Objetiva Regresiva y su repercusión en las poblaciones vectoras en la estación meteorológica de Caibarién, Villa Clara, Cuba. Biotempo. 2018;15(1):23-30. http://doi.org/d5n7.
13.Fimia-Duarte R, Osés-Rodríguez R, Iannacone J, Carmenate-Ramírez A, Diéguez-Fernández L, González-González R, et al. Modelación y predicción hasta el año 2020 para la angiostrongilosis total utilizando la Regresión Objetiva Regresiva. Villa Clara, Cuba. In: ABSTRACT BOOK del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) “Impacto del Cambio Climático en las Enfermedades Parasitarias”. Lima: The Biologis; 2017.
14.Osés-Rodríguez R, Fimia-Duarte R, Iannacone J, Argota-Pérez G, Cruz-Camacho L, Domínguez-Hurtado I. Impacto de la temperatura en la presencia de infecciones respiratorias de aves en un país tropical. Biotempo. 2017;14(1):17-25. http://doi.org/d5n8.
15.Fimia-Duarte R, Aldaz-Cárdenas JW, Aldaz-Cárdenas NG, Segura-Ochoa JJ, Segura-Ochoa JJ, Cepero-Rodríguez O, et al. Mosquitoes (Diptera: Culicidae) and their control by means of biological agents in Villa Clara province, Cuba. International Journal of Current Research. 2016;8(12):43114-20.
16.Fimia-Duarte R, Castañeda-López W, González-González R, Fábrega-Obregón G, Iannacone J, Ramos-López-Silvero C, et al. Entomofauna de mosquitos (Diptera: Culicidae) de las provincias Sancti Spíritus y Villa Clara, Cuba. The Biologist (Lima). 2015;13(2):173-182.
17.Organización Mundial de la Salud (OMS). Resistencia de los vectores de enfermedades a plaguicidas. Quinto Informe del Comité de Expertos de la OMS en Biología de los Vectores y Lucha Antivectorial. Ginebra: Series de Informes Técnicos No. 655; 1980.
18.Ibáñez-Bernal S, Martínez-Campos C. Clave para la identificación de las larvas de mosquitos comunes en las áreas urbanas y suburbanas de la República Mexicana (Diptera: Culicidae). Folia Entomol Mex. 1994;92:43-73.
19.Forattini OP. Culicidología Médica. Principios generales, morfología y glosario taxonómico. Sao Paulo: Universidad de Sao Paulo; 1996.
20.González Broche R. Culícidos de Cuba. La Habana Editorial Científico-Técnica; 2006.
21.Gaffigan TV, Wilkerson RC, Pecor JE, Stoffer JA, Anderson T. Systematic Catalog of Culicidae. Walter Reed Biosystematics Unit. Silver Spring, MD: 2015 [cited 2020 Jul 30]. Available from: https://bit.ly/2PmTIuF.
22.Osés-Rodríguez R, Fimia-Duarte R, Aldaz-Cárdenas JW, Iannacone-Oliver J, Zaita-Ferrer Y, Osés-Llanes C, et al. Modelación matemática del cólera por medio de la Regresión Objetiva Regresiva y su relación con las variables climáticas. Caibarién, Villa Clara, Cuba. In: Abstract Book del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) “Impacto del Cambio Climático en las Enfermedades Parasitarias”. Lima: The Biologis; 2017.
23.Sánchez-Álvarez ML, Osés-Rodríguez R, Fimia-Duarte R, Gascón-Rodríguez BC, Iannacone J, Zaita-Ferrer Y, et al. La Regresión Objetiva Regresiva más allá de un ruido blanco para los virus que circulan en la provincia Villa Clara, Cuba. In: ABSTRACT BOOK del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) “Impacto del Cambio Climático en las Enfermedades Parasitarias”. Lima: The Biologis; 2017.
24.Pérez-Menzies M, Cutiño-Alba Y, Cid-Acosta Y, Torres-Guayanes G, Castillo-Quesada RM, Alfonso-Herrera Y, et al. Presencia larval de Culex (Culex) interrogator (Dyar and Knab) (Diptera:Culicidae) en Cuba. Rev Cubana Med Trop. 2019;70(3):108-13 Available from: https://bit.ly/3fqFXWe.
25.Marquetti-Fernández MC. Aspectos bioecológicos de importancia para el control de Aedes aegypti y otros culícidos en el ecosistema urbano [dissetation]. Ciudad de La Habana: Departamento de Control de Vectores, Instituto de Medicina Tropical “Pedro Kourí”; 2006.
26.García-Ávila I. Fauna cubana de mosquitos y sus criaderos típicos. La Habana: Dirección de Publicaciones de la Academia de Ciencias de Cuba; 1977.
27.González-Broche R. Nuevos reportes sobre la tribu Sabethini (Diptera: Culicidae) para Cuba. Poeyana. 1985;285:1-11.
28.Mattingly PF. The urban mosquito hazard today. Bull World Health Organ.1963;29(Suppl 1):135-9.
29.Scorza JV. Observaciones bionómicas sobre Culex pipiens fatigans Wied, 1821 de Venezuela. Mérida: Universidad de Los Andes; 1972.
30.Cruz-Pineda CA, Cabrera-Carmenate MV. Caracterización entomológica-ecológica de casos y sospechosos del virus del Nilo Occidental en la provincia Sancti Spíritus, Cuba. Rev Cubana Med Trop. 2006;58(3):235-40.
31.Pupo-Antúnez M, Cabrera-Rodríguez V, Vázquez-Mojena Y, Drebor-M, Andonova M, Dickinson-Meneses F, et al. Estudio serológico en localidades cubanas con infecciones confirmadas al virus del Nilo Occidental. Rev Cubana Med Trop. 2011;63(3):227-30.
32.Bennett KL, Shija F, Linton YM, Misinzo G, Kaddumukasa M, Djouaka R, et al. Historical environmental change in Africa drives divergence and admixture of Aedes aegypti mosquitoes: a precursor to successful worldwide colonization? Mole Ecol. 2016;25(17):4337-54. http://doi.org/f84q3m.
33.Bezerra JMT, Araújo RGP, Melo FF, Gonçalves CM, Chaves BA, Silva BM, et al. Aedes (Stegomyia) albopictus dynamics influenced by spatiotemporal characteristics in a Brazilian dengue-endemic risk city. Acta Trop. 2016;164:431-7. http://doi.org/f9c5sf.
34.Guzmán MG, Álvarez M, Halstead SB. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection. Arch Virol. 2013;158(7):1445-59. http://doi.org/bk76.
35.Bangs MJ, Larasati RP, Corwin AL, Wuryadi S. Climatic factors associated with epidemic dengue in Palembang, Indonesia: Implications of shor-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health. 2006;37(6):1103-16.
36.Fimia Duarte R, Marquetti Fernández Mc, Iannacone J, Hernández Contreras N, González Muñoz G, Poso del Sol MC, et al. Factores antropogénicos y ambientales sobre la fauna de culícidos (Diptera: Culicidae) de la provincia Sancti Spíritus, Cuba. The Biologist. 2015;13(1):53-74.
37.Rodríguez-Sosa MA, Rueda J, Vásquez-Bautista YE, Fimia-Duarte R, Borge-de Prada M, Guerrero KA, et al. Diversidad de mosquitos (Diptera: Culicidae) de Jarabacoa, República Dominicana. Graellsia. 2019;75(1):e084.
38.Osés-Rodríguez R, Burgos-Alemán I, Osés-Llanes C, Otero--Martin M, Fimia-Duarte R, Cruz-Camacho L. How much methodology ROR explains the rain errors in Caibarién, Cuba. International Journal of Current Advanced Research. 2015;4(2):17-21.
39.Osés-Rodríguez R, Fimia-Duarte R, Cruz-Camacho L. Simple ModeltoEstimate Longitude and Area of Universe. Open Access Library Journal.2015;2:e1334. http://doi.org/d5pf.
40.Osés-Rodríguez R, Fimia-Duarte R, Iannacone J, Saura-Gonzáles G, Gómez-Camacho L, Ruiz-Cabrera N. Modelación de la temperatura efectiva equivalente para la estación del Yabú y para la densidad larval total de mosquitos en Caibarién, provincia Villa Clara, Cuba. Rev Peruana Entomol. 2016;51(1.2):1-7.
41.Fimia-Duarte R, González-González R, Cepero-Rodríguez R, Valdés-Álvarez M, Osés-Rodríguez R, Corona-Santander E, et al. Influencia de algunas variables climáticas sobre la malacofauna fluvial con importancia zoonótica en la provincia Villa Clara. REDVET Revista Electrónica de Veterinaria. 2012 [cited 2020 Aug 3];13(7). Available from: https://bit.ly/33lLACy.
42.Alkhaldy I. Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia - a generalised linear model with break-point analysis. Acta Trop. 2017;168:9-15. http://doi.org/f9w77f.
43.García-Gutiérrez S, Pérez-Bastida JA, Fimia-Duarte R, Osés-Rodríguez R, Garín-Landa GM, González-González R. Influencia de algunas variables climatológicas sobre las densidades larvarias en criaderos de culícidos. Pol Cap. Roberto Fleites 2009-2010. REDVET Revista Electrónica de Veterinaria. 2012 [cited 2020 Aug 3];13(5). Available from: https://bit.ly/3k9Uc5q.
44.Fimia-Duarte R, Osés-Rodríguez R, Carmenate-Ramírez A, Iannacone J, González-González R, Gómez-Camacho L, et al. Modelación y predicción para moluscos con angiostrongilosis en la provincia Villa Clara, Cuba utilizando la regresión objetiva regresiva (ROR). Neotropical Helminthology. 2016;10(1):61-71.
Recibido: 6 de mayo de 2019; Aceptado: 31 de julio de 2019
Abstract
Introduction:
Mosquitoes (Diptera: Culicidae) are considered one of the most versatile organisms in the world as they can breed in any pool of water, such as puddles or tanks. However, their reproduction is influenced by atmospheric variables that allow predicting their population density.
Objective:
To assess the impact of atmospheric pressure on mosquito population density in the province of Villa Clara, Cuba, by means of a mathematical model based on a regressive objective regression (ROR) methodology.
Materials and methods:
The development of the mathematical model to predict breeding sites was based on the number of breeding sites reported in the province of Villa Clara between 2000 and 2017, and the ROR model. Furthermore, a regression analysis was carried out using the IBM SPSS® version 19.0, which allowed obtaining models that explained 100% of the variance, with its corresponding standard error.
Results:
With respect to the number of breeding sites, an increasing trend was observed in the municipality of Cifuentes, while the trend was downward in Ranchuelo and Caibarién. The municipalities of Santa Clara and Encrucijada had the highest and lowest standard deviation (13.432 vs. 5.968, respectively), which demonstrates great variability among the data of each municipality.
Conclusions:
There is a close relationship between atmospheric pressure and mosquito population density since both total and specific larval densities increase as atmospheric pressure increases.
Keywords:
Cuba, Culicidae, Forecasting, Disease Vectors (MeSH).Resumen
Introducción.
Los mosquitos (Diptera: Culicidae) son uno de los organismos más versátiles del mundo, pues pueden reproducirse en cualquier depósito de agua, como charcos o tanques. Sin embargo, su reproducción está influenciada por variables atmosféricas que permiten predecir su densidad poblacional.
Objetivo.
Evaluar el impacto de la presión atmosférica en la densidad poblacional de mosquitos en la provincia de Villa Clara, Cuba, mediante el uso de modelos matemáticos basados en la metodología de regresión objetiva regresiva (ROR).
Materiales y métodos.
El desarrollo del modelo matemático de pronóstico de focos de reproducción se basó en el número de focos reportados en la provincia de Villa Clara entre 2000 y 2017, y en el modelo ROR. Además, se realizó un análisis de regresión mediante el programa IBM SPSS® versión 19.0, lo que permitió obtener modelos de regresión que explicaron el 100% de la varianza, con su error típico.
Resultados.
Respecto a la cantidad de focos, se observó una tendencia al aumento en el municipio de Cifuentes, mientras que en Ranchuelo y Caibarién la tendencia fue a la reducción. Los municipios de Santa Clara y Encrucijada tuvieron la desviación estándar más alta y más baja, respectivamente (134.32 vs. 5.968), lo que evidencia una gran variabilidad entre los datos de cada municipio.
Conclusiones.
Existe una estrecha relación entre la presión atmosférica y la densidad poblacional de mosquitos, ya que a medida que aumenta la presión atmosférica, aumentan las densidades larvales, tanto total como específicas.
Palabras clave:
Cuba, Culicidae, Predicción, Vectores de enfermedades (DeCS).Introduction
Throughout history, humanity has faced multiple potentially deadly viral and parasitic diseases, including yellow fever, dengue, zika, chikungunya, and malaria, which are closely related because they are transmitted by a mosquito (Diptera: Culicidae). These diseases are highly prevalent in tropical areas and the risk of contracting them depends, to a large extent, on rainfall, temperature and the living conditions of each place.2
Mosquitoes are considered one of the most versatile living organisms in the world as they can breed in any pool of water, such as puddles or tanks; however, their reproduction is influenced by certain biotic and abiotic factors.3,4These insects need the blood of humans and/ or other vertebrates to make their eggs fertile and viable, so they represent a threat to humanity since they are vectors of pathogenic microorganisms of several vector-borne diseases that can become burdens for many countries from the health, economic and social point of view.5,6
Natural disasters threaten the safety of people, hurricanes being the most destructive phenomenon in the tropics because of all that their passage entails.7-10 Since the frequency of these events is on the rise, knowing their behavior is essential; for this reason, in Cuba, seasonal forecasts are constantly made based on the oceanic and atmospheric conditions that preceded the past hurricane seasons (active or inactive), as they provide useful information about trends that could be observed in similar future seasons.11
Although the occurrence of hurricanes cannot be predicted accurately, their impact can be determined in advance. Moreover, research in this area has made significant progressin recent years, so much so that in 2012, for the first time, it was possible to forecast an event of this type a year in advance.12 To this end, high-quality forecasts are made using the regressive objective regression (ROR) methodology, which, due to its simplicity and accuracy, can be useful to know the future of climate variables or daily data years in advance.13,14 Forecast times can extend to 11 years of the solar cycle or other longer cycles known in nature.
By using the ROR methodology, the population dynamics of insects such as mosquitoes, and their interactions with certain environmental variables, can also be modeled. This is helpful to establish timely control and prophylactic measures in epidemiological surveillance programs.12,13,15
In this sense, the objective of this research was to assess the impact of atmospheric pressure on mosquito population density in the province of Villa Clara, Cuba, through mathematical models based on the ROR methodology.
Materials and methods
Area and period of study
The study was carried out between 2007 and 2017 in Villa Clara, a province located in the central region of Cuba, bordering on the north with the Atlantic Ocean and on the east, west and south with the provinces of Sancti Spíritus, Matanzas and Cienfuegos, respectively. Politically and administratively speaking, Villa Clara has 13 municipalities, and its capital is Santa Clara (Figure 1).
Figure 1: Political map of Cuba and the province of Villa Clara. Source: Own elaboration.
Currently, Villa Clara has five weather stations located in Sagua la Grande (latitude 22°48'17" N and longitude 80°05'32'' W), La Piedra (latitude 22°06'48'' N and longitude 79°58'48'' W), Santo Domingo (latitude 22°35'08'' N and longitude 80°13'35'' W), Caibarién (latitude 22°29'48'' N and longitude 78°28'14'' W) and El Yabú (latitude 22°27'43'' N and longitude 79°59'30'' W), the latter located in the city of Santa Clara.
Catching and identifying mosquitoes
Trees and shrubs located on the margins of river ecosystems, as well as discarded artificial water-holding containers (cans, bottles, tires, etc.), were inspected to capture mosquitoes in preimaginal stages. Samples were collected in rivers, streams, ditches, gullies, lagoons, marshes and swamps, with special emphasis on banks because they are usually quiet and shallow environments where mosquitoes breed.16 Similarly, in order to compare the results obtained with the information available in the area and establish possible coincidences, the records and maps of the Unidad Provincial de Vigilancia y Lucha Antivectorial (Provincial Unit for Antivectorial Fight and Surveillance - UPVLA) of Villa Clara were consulted, as it archives the entomological records of the region.
Larvae and pupae were captured using ladles17 and transferred to the laboratory in 250mL plastic bottles duly labeled; general larval density was considered for this process. On the other hand, adult mosquitoes were captured using light traps and were transported in 50 mL glass jars, which contained absorbent cotton balls impregnated with ether and/or chloroform.
Preimaginal and adult specimens captured were identified in the Medical Entomology Laboratory of the UPVLA according to dichotomous and pictorial access keys18-20 and following the taxonomic classification of the Systematic Catalog of Culicidae of the Walter Reed Bio-systematics Unit.21 As part of the research, the annual number (2007-2017) of breeding sites per municipality in the province was considered and the impact of atmospheric pressure on mosquito population in the Santo Domingo station was analyzed since the results can be extrapolated to the rest of the municipalities in the province due to their geographical proximity.
Statistical analysis
The ROR methodology, which consists of several steps, was used to develop the mathematical model to predict breeding sites.22,23 First, sawtooth (S) and inverted sawtooth (IS) dichotomous variables were created for the number of cases (NoC) in the database used for ROR modeling. The NoC coefficient in the model represented the trend of the series and the variable to model was the number of mosquito breeding sites. Thus, the variability of the number of breeding sites was established by S and IS coefficients plus the NoC coefficient, explaining a large percentage of the variance (close to 100%). The S value was 1 for odd NoC, the IS value was 0 for even NoC, and vice versa.
Later, a regression analysis was performed using the statistical package SPSS version 19.0, specifically with the ENTER method, which allowed obtaining the predicted variable and the ERROR.
Then, the autocorrelogram of the variable ERROR was obtained taking into account the maximum values of the partial autocorrelation function (PACF). New variables were then calculated based on the significant lag of the PACF and were included in a new multivariate linear regression analysis, where they were regressed in a successive approximation process until a white noise was obtained in the regression errors. In the case of the atmospheric pressure, one-year lags were used, as done by other authors for climatic indexes.10,22,23 It should be noted that, in the Provincial Meteorological Center of Villa Clara, the atmospheric pressure values are recorded every three hours.
For mathematical modeling, information on the number of breeding sites reported annually in each of the municipalities of Villa Clara between 2007 and 2017 was taken into account; these data were analyzed through descriptive statistics: the minimum and maximum values were established and the average was estimated.
Results
In the present research work, 43 species of mosquitoes of 13 genera were identified in Villa Clara. Several of these species had marked ubiquity (Table 1).
* Each municipality was identified with a number: 1 : Corralillo, 2: Quemado de Güines, 3: Sagua la Grande, 4: Encrucijada, 5: Camajuaní, 6: Caibarién, 7: Remedios, 8: Placetas, 9: Santa Clara, 10: Cifuentes, 11: Santo Domingo, 12: Ranchuelo, and 13: Manicaragua. Source: Own elaboration.Table 1: Distribution of the identified mosquito species in the province of Villa Clara, Cuba. 2007-2017.
Mosquito species
Classification and year
Municipalities *
Total number of municipalities
Aedeomyia squamipennis
Lynch Arribálzaga, 1878
9, 12
2
Anopheles albimanus
Wiedemann, 1821
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Anopheles atropos
Dyar & Knab, 1906
5, 6
2
Anopheles grabhamii
Theobald, 1901
5 , 6,11
3
Anopheles vestitipennis
Dyar & Knab, 1906
3, 5, 6, 7, 8, 9, 11
7
Anopheles crucians
Wiedemann, 1828
5 , 8,12
3
Aedes aegypti
Linnaeus, 1762
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Aedes albopictus
Skuse, 1894
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Aedes mediovittatus
Coquillett, 1906
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Aedes scapularis
Rondani, 1848
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
12
Aedes sollicitans
Walker, 1856
1, 3, 4, 5, 6, 7, 10, 11
8
Aedes taeniorhynchus
Wiedemann, 1821
1, 2, 3, 4, 5, 6, 7, 10, 11
9
Aedes tortilis
Theobald, 1903
3, 4, 5, 7, 9
5
Aedes walkeri
Theobald, 1901
2, 6, 11, 12
4
Coquillettidia nigricans
Coquillett, 1904
9, 11
2
Culex americanus
Neveu-Lemaire, 1902
6, 9
2
Culex atratus
Theobald, 1901
4, 5, 6, 8, 9, 10
6
Culex bahamensis
Dyar & Knab, 1906
6, 8
2
Culex cancer
Theobald, 1901
1, 5 , 6
3
Cx. chidesteri
Dyar, 1921
1, 2, 6, 8, 9, 11, 12
7
Culex corniger
Theobald, 1903
2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 13
12
Culex erraticus
Dyar & Knab, 1906
4, 5, 6, 7, 8, 9, 10, 12, 13
9
Culex iolambdis
Dyar, 1918
8, 9
2
Culex nigripalpus
Theobald, 1901
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Culex pilosus
Dyar & Knab, 1906
1, 3, 4, 5, 6, 8, 13
7
Culex quinquefasciatus
Say, 1823
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Culex sphinx
Howard, Dyar & Knab, 1915
6
1
Culex secutor
Theobald, 1901
8, 13
2
Mansonia titillans
Walker, 1848
3, 6, 8, 9, 10, 11, 12
7
Limatus durhamii
Theobald, 1901
9, 12
2
Orthopodomyia signifera
Coquillett, 1896
8, 12
2
Psorophora ciliata
Fabricius, 1794
1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13
12
Psorophora confinnis
Lynch Arribálzaga, 1891
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13
13
Psorophora howardii
Coquillett, 1901
6, 7, 8, 9, 10, 12, 13
7
Psorophora johnstonii
Grabham, 1905
6
1
Psorophora pygmaea
Theobald, 1903
1, 4, 5, 6, 7, 9, 10, 13
8
Psorophora santamarinai
González Broche, 2000
6
1
Psorophora insularia
Dyar and Knab, 1906
6
1
Psorophora infinis
Dyar and Knab, 1906
8
1
Toxorhynchites portoricensis
von Roder, 1885
5, 6
2
Uranotaenia sapphirina
Osten-Sacken, 1868
3, 4, 5, 6, 7, 8, 9, 11, 12
9
Wyeomyia vanduzeei
Dyar & Knab, 1906
9
1
Wyeomyia mitchellii
Theobald, 1905
8, 9
2
Table 2 presents the descriptive statistics for the number of breeding sites per municipality; it is possible to see that the highest mean and standard deviation (σ) are found in the municipality of Santa Clara and that the lowest are in Encrucijada. The maximum value obtained for the time series (11 years of annual data) was 8 778 in Santa Clara, while the minimum value was 0 in Corralillo, Quemado de Güines, Encrucijada and Caibarién. The overall average number of breeding sites in the province was 7 143, with a σ of 2 165.
σ: standard deviation; N: number of years studied from 2007 to 2017. Source: Own elaboration.Table 2: Descriptive statistics of the number of annual breeding sites in the province of Villa Clara, Cuba 2007-2017.
Municipalities
N
Minimum
Maximum
Mean
σ
Corralillo
11
0.00
44.00
12.72
15.49
Quemado de Güines
11
0.00
117.00
17.00
34.33
Sagua la Grande
11
4.00
1 199.00
475.27
468.07
Encrucijada
11
0.00
14.00
6.36
5.35
Camajuaní
11
1.00
108.00
44.36
36.37
Caibarién
11
0.00
54.00
18.81
18.55
Remedios
11
1.00
249.00
52.00
76.57
Placetas
11
10.00
438.00
89.45
126.31
Santa Clara
11
3 734.00
8 778.00
5 987.63
1 549.21
Cifuentes
11
3.00
78.00
25.72
25.29
Santo Domingo
11
5.00
204.00
91.09
80.06
Ranchuelo
11
36.00
675.00
185.81
214.27
Manica ragua
11
6.00
381.00
137.09
145.17
Provincia
11
4 497.00
10 806.00
7 143.36
2 165.59
The ROR model was chosen because, during the investigation, it was concluded that by combining lags 1, 2 and 6 (corresponding to 1 year, 2 years and 6 years, respectively) a perfect model could be obtained for each municipality but the parameters of influence could not be measured since there were divisions by zero. The proposed model proved to be perfect according to the analyzed data since no standard error was observed and no autocorrelation was detected in the residues by means of the Durbin Watson statistic. The analysis of variance (ANOVA) for the model indicated that Fisher's F could not be determined because the error was zero, and to calculate this statistic, it is necessary to divide by the error, which creates a mathematical uncertainty.
Figure 2 shows the result of the modeling for the province; it is possible to see that the predicted value has excellent coincidence with the real value from 2013 to 2017.
Figure 2: Real and predicted value of the number of mosquito breeding sites in the province of Villa Clara, Cuba.
The highest variance explained was the same for all municipalities (100%), but there was an upward trend in Cifuentes since the NoC coefficient was positive, while the trend was downward in Caibarién and Ranchuelo since the NoC coefficient was negative (Table 3). NoC coefficient was recorded when it was not significant. The ANOVA was only doneforthe larval stage.
S: sawtooth; IS: inverted sawtooth; NoC: number of cases. * Lag 1, Lag 2, and Lag 6 are the predicted lags, which in this case are 1 year, 2 years and 6 years. Source: Own elaboration.Table 3: Number of breeding sites per municipality for larval density in the province of Villa Clara, Cuba 2007-2017.
Municipality and significance of larval density
Durbin Watson
S
IS
NoC
Lag2*
Lagl *
Lag6 *
Corralillo 0.002
1.459
16.99
2.709
-1.09
1.936
-2.198
Quemado de Güines 0.001
1.889
22.85
3
0.148
-3.889
18.407
Sagua la Grande 0.003
0.892
2 400
3 919
-2.359
-1.363
6.348
Encrucijada 0.004
2.184
5.968
6.437
-0.482
0.378
-0.194
Camajuaní 0.023
1.655
141.74
65.98
-1.996
0.802
0.534
Caibarién 0.014
2.044
-14.877
-4.604
3.378
-1.375
5.901
Remedios 0.025
1.328
160.44
300.25
-2.887
-0,601
6.846
Placetas 0.004
1.808
126.88
-14.28
-2.776
-1.447
12.969
Santa Clara 0.005
1.397
13 432
12 753
-0.431
-0.755
0.008
Cifuentes 0.032
1.567
143.35
-136.42
19.708
0.807
-0.986
Santo Domingo 0.002
1.570
356.31
399.82
-0.751
-1.998
15.040
Ranchuelo 0.004
1.630
-78.03
-130.33
-33.253
0.253
10.685
Manicaragua 0.004
2.415
418.46
467.47
-1.296
-0.151
0.482
Province
1.268
14 114
12 952
-0.546
-0.886
0.875
The forecast values obtained using the atmospheric pressure prediction in the Santo Domingo station showed a behavior similar to that calculated without it (Figure 3).
Figure 3: Real and predicted value of the amount of mosquitoes' breeding sites in the entire province of Villa Clara, Cuba, using only the predicted value of the atmospheric pressure at sea level in the Santo Domingo station.
Discussion
From the 70 species of mosquitoes identified in Cuba so far,16,24 43 were found in Villa Clara in the present study.
The most common genera with the highest presence in the municipalities studied were Anopheles, Culex and Psorophora, while the most common and widely distributed species were Anopheles albimanus, Culex quinquefasciatus, Culex nigripalpus, Aedes mediovittatus, Psorophora confinnis, Aedes aegypti and Aedes albopictus (found in the 13 municipalities), followed by Culex corniger, Aedes scapularis and Psorophora ciliata (found in 12 of the 13 municipalities). The most common species were An. albimanus, Anopheles crucians, Culex atratus, Cx. quinquefasciatus, Cx. nigripalpus and Ps. Confinnis, which were found in practically all the ecosystems sampled and in relatively high abundance, a fact that coincides with what was reported by Mar-quetti-Fernández,25 specifically for Cx. quinquefasciatus in urban ecosystems. The great phenotypic plasticity of the mosquitoes was also evident since it was found that they are capable of exploiting any habitat.16,26-30
Ae. aegypti and Ae. albopictus are largely found in Villa Clara, which is noteworthy because they are high-risk entomoepidemiological species due to their involvement in infectious diseases such as dengue fever, yellow fever, West Nile fever, chikungunya and Zika virus.31-34 Currently, these two species are present practically in the entire Cuban territory and they are increasingly expanding because human activity generates an important number of breeding sites.33 This expansion shows their high phenotypical plasticity and great capacity of adaptation to different ecological niches.35-37
According to the descriptive statistics, there is great variability between municipalities regarding the number of breeding sites per municipality (Table 2), which may be explained by the physical and geographical characteristics of each place, as well as the quality of the samples, aspects that are consistent with the results of previous research.30,31
Considering that the value obtained in the present study using the Fisher's F test was high, the model designed here to establish mosquito population density is extremely good since the higher the Fisher's F, the higher the probability of having a good model in the ANOVA. 38,39
During the research, influence parameters could not be measured because they were divided by zero, so it was necessary to resort to mathematical definitions made by other authors and even for other organisms.40,41 The greatest variance explained was the same for all municipalities and it was 100%. Moreover, as mentioned above, there was a trend towards a decrease in the number of breeding sites in Caibarien and Ranchuelo, while there was an upward trend in Cifuentes. The most significant predicted lags were at 1 year, 2 years and 6 years (Table 3), which coincides with other studies where similar results were obtained and good modeling results were achieved through ROR models.42-44
The impact of the atmospheric pressure predicted in the four stations using the ROR methodology explains 100% of the variance in this new model. From all the pressure measurements per station obtained, the model selected the atmospheric pressure registered in the Santo Domingo station, which resulted in an impact coefficient of 13.84. Therefore, as atmospheric pressure increased in Santo Domingo, the number of breeding sites in the provinces increased; this coincides with previous works carried out in other municipalities of the province to measure larval density9,12,40 and confirms the impact of atmospheric pressure and, consequently, the performance of the North Atlantic anticyclone, which is the main climate modulator in the island of Cuba.
Conclusions
There is a close relationship between atmospheric pressure and mosquito population density since both total and specific larval densities increase as atmospheric pressure increases. These findings confirm the possibility of mathematically modeling the effect of atmospheric pressure on mosquito population density (Diptera: Culicidae) in Villa Clara, Cuba.
Acknowledgments
None stated by the authors.
References
Referencias
Troyo A, Calderón-Arguedas O, Fuller DO, Solano ME, Avedaño A, Arheart KL, et al. Seasonal profiles of Aedes aegypti (Diptera: Culicidae) larval habitats in an urban area of Costa Rica with a history of mosquito control. J Vector Ecol. 2008;33(1):76-88. http://doi.org/fjfw4j.
Organización Mundial de la Salud (OMS). Dengue y dengue grave. Nota descriptiva. Ginebra: OMS; 2019 [cited 2020 Aug 3]. Available from: https://bit.ly/3gt8w6H.
Kleinman S, Glynn SA, Busch M, Todd D, Powell L, Pietrelli L, et al. The 2003 West Nile virus UnitedState epidemic: the America's Blood Centers experience. Transfusion. 2005;45(4):469-79. http://doi.org/d64wtj.
Nutt C, Adams P. Zika in Africa-the invisible epidemic? Lancet. 2017;389(10079):1595-6. http://doi.org/ggx92m.
Wasserman S, Tambyah PA, Lim PL. Yellow fever cases in Asia: primed for an epidemic. Int J Infect Dis. 2016;48:98-103. http://doi.org/f8sdvk.
Weaver SC, Forrester NL. Chikungunya: Evolutionary history and recent epidemic spread. Antiviral Res.2015;120:32-9. http://doi.org/gf2kr5.
Adu-Prah S, Kofi-Tetteh E. Spatiotemporal analysis of climate variability impacts on malaria prevalence in Ghana. Appl Geogr. 2015;60:266-73. http://doi.org/f7gjh9.
Chaves LF, Morrison AC, Kitron UD, Scott TW. Nonlinear impacts of climatic variability on the density-dependent regulation of an insect vector of disease. Glob Chan Biol. 2012;18(2):457-68. http://doi.org/c9chgq.
Osés-Rodríguez R, Fimia-Duarte R, Pedraza-Martínez FA, Saura-González G, Otero-Martín M, Jiménez-Lorenzo F. Modelación de la densidad larvaria total de mosquitos (Diptera: Culicidae) utilizando tres modelos en la provincia de Villa Clara, Cuba. REDVET Rev. Electrón. vet. 2014 [cited 2020 Aug 3];15(8). Available from: https://bit.ly/2Dj3rQ7.
Osés-Rodríguez R, Aldaz-Cárdenas JW, Fimia-Duarte R, Segura-Ochoa JJ, Aldaz-Cárdenas NG, Segura-Ochoa JJ, et al. The ROR's methodology an it's possibility to find information in a white noise. International Journal of Current Research. 2017;9(3):47378-82.
Ortiz-Bultó PL, Pérez-Rodríguez A, Rivero-Valencia A, León-Vega N, Díaz-González M, Pérez-Carrera A. Assessment of human health vulnerability to climate variability and change in Cuba. Environ Health Perspect. 2006;114(12):1942-9.
Osés-Rodríguez R,Otero-Martin M, Ruiz Cabrera N, Fimia-Duarte R, Iannacone J. Pronóstico para el huracán Irma por medio de la Regresión Objetiva Regresiva y su repercusión en las poblaciones vectoras en la estación meteorológica de Caibarién, Villa Clara, Cuba. Biotempo. 2018;15(1):23-30. http://doi.org/d5n7.
Fimia-Duarte R, Osés-Rodríguez R, Iannacone J, Carmenate-Ramírez A, Diéguez-Fernández L, González-González R, et al. Modelación y predicción hasta el año 2020 para la angiostrongilosis total utilizando la Regresión Objetiva Regresiva. Villa Clara, Cuba. In: ABSTRACT BOOK del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) "Impacto del Cambio Climático en las Enfermedades Parasitarias". Lima: The Biologis; 2017.
Osés-Rodríguez R, Fimia-Duarte R, Iannacone J, Argota-Pérez G, Cruz-Camacho L, Domínguez-Hurtado I. Impacto de la temperatura en la presencia de infecciones respiratorias de aves en un país tropical. Biotempo. 2017;14(1):17-25. http://doi.org/d5n8.
Fimia-Duarte R, Aldaz-Cárdenas JW, Aldaz-Cárdenas NG, Segura-Ochoa JJ, Segura-Ochoa JJ, Cepero-Rodríguez O, et al. Mosquitoes (Diptera: Culicidae) and their control by means of biological agents in Villa Clara province, Cuba. International Journal of Current Research. 2016;8(12):43114-20.
Fimia-Duarte R, Castañeda-López W, González-González R, Fábrega-Obregón G, Iannacone J, Ramos-López-Silvero C, et al. Entomofauna de mosquitos (Diptera: Culicidae) de las provincias Sancti Spíritus y Villa Clara, Cuba. The Biologist (Lima). 2015;13(2):173-182.
Organización Mundial de la Salud (OMS). Resistencia de los vectores de enfermedades a plaguicidas. Quinto Informe del Comité de Expertos de la OMS en Biología de los Vectores y Lucha Antivectorial. Ginebra: Series de Informes Técnicos No. 655; 1980.
Ibáñez-Bernal S, Martínez-Campos C. Clave para la identificación de las larvas de mosquitos comunes en las áreas urbanas y suburbanas de la República Mexicana (Diptera: Culicidae). Folia Entomol Mex. 1994;92:43-73.
Forattini OP. Culicidología Médica. Principios generales, morfología y glosario taxonómico. Sao Paulo: Universidad de Sao Paulo; 1996.
González Broche R. Culícidos de Cuba. La Habana Editorial Científico-Técnica; 2006.
Gaffigan TV, Wilkerson RC, Pecor JE, Stoffer JA, Anderson T. Systematic Catalog of Culicidae. Walter Reed Biosystematics Unit. Silver Spring, MD: 2015 [cited 2020 Jul 30]. Available from :http https://bit.ly/2PmTIuF.
Osés-Rodríguez R, Fimia-Duarte R, Aldaz-Cárdenas JW, Iannacone-Oliver J, Zaita-Ferrer Y, Osés-Llanes C, et al. Modelación matemática del cólera por medio de la Regresión Objetiva Regresiva y su relación con las variables climáticas. Caibarién, Villa Clara, Cuba. In: ABSTRACT BOOK del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) "Impacto del Cambio Climático en las Enfermedades Parasitarias". Lima: The Biologis; 2017.
Sánchez-Álvarez ML, Osés-Rodríguez R, Fimia-Duarte R, Gascón-Rodríguez BC, Iannacone J, Zaita-Ferrer Y, et al. La Regresión Objetiva Regresiva más allá de un ruido blanco para los virus que circulan en la provincia Villa Clara, Cuba. In: ABSTRACT BOOK del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO) "Impacto del Cambio Climático en las Enfermedades Parasitarias". Lima: The Biologis; 2017.
Pérez-Menzies M, Cutiño-Alba Y, Cid-Acosta Y, Torres-Guayanes G, Castillo-Quesada RM, Alfonso-Herrera Y, et al. Presencia larval de Culex (Culex) interrogator (Dyar and Knab) (Diptera:Culicidae) en Cuba. Rev Cubana Med Trop. 2019;70(3):108-13 Available from: https://bit.ly/3fqFXWe.
Marquetti-Fernández MC. Aspectos bioecológicos de importancia para el control de Aedes aegypti y otros culícidos en el ecosistema urbano [dissetation]. Ciudad de La Habana: Departamento de Control de Vectores, Instituto de Medicina Tropical “Pedro Kourí”; 2006.
García-Ávila I. Fauna cubana de mosquitos y sus criaderos típicos. La Habana: Dirección de Publicaciones de la Academia de Ciencias de Cuba; 1977.
González-Broche R. Nuevos reportes sobre la tribu Sabethini (Diptera: Culicidae) para Cuba. Poeyana. 1985;285:1-11.
Mattingly PF. The urban mosquito hazard today. Bull World Health Organ.1963;29(Suppl 1):135-9.
Scorza JV. Observaciones bionómicas sobre Culex pipiens fatigans Wied, 1821 de Venezuela. Mérida: Universidad de Los Andes; 1972.
Cruz-Pineda CA, Cabrera-Carmenate MV. Caracterización entomológica-ecológica de casos y sospechosos del virus del Nilo Occidental en la provincia Sancti Spíritus, Cuba. Rev Cubana Med Trop. 2006;58(3):235-40.
Pupo-Antúnez M, Cabrera-Rodríguez V, Vázquez-Mojena Y, Drebor-M, Andonova M, Dickinson-Meneses F, et al. Estudio serológico en localidades cubanas con infecciones confirmadas al virus del Nilo Occidental. Rev Cubana Med Trop. 2011;63(3):227-30.
Bennett KL, Shija F, Linton YM, Misinzo G, Kaddumukasa M, Djouaka R, et al. Historical environmental change in Africa drives divergence and admixture of Aedes aegypti mosquitoes: a precursor to successful worldwide colonization? Mole Ecol. 2016;25(17):4337-54. http://doi.org/f84q3m.
Bezerra JMT, Araújo RGP, Melo FF, Gonçalves CM, Chaves BA, Silva BM, et al. Aedes (Stegomyia) albopictus dynamics influenced by spatiotemporal characteristics in a Brazilian dengue-endemic risk city. Acta Trop. 2016;164:431-7. http://doi.org/f9c5sf.
Guzmán MG, Álvarez M, Halstead SB. Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection. Arch Virol. 2013;158(7):1445-59. http://doi.org/bk76.
Bangs MJ, Larasati RP, Corwin AL, Wuryadi S. Climatic factors associated with epidemic dengue in Palembang, Indonesia: Implications of shor-term meteorological events on virus transmission. Southeast Asian J Trop Med Public Health. 2006;37(6):1103-16.
Fimia Duarte R, Marquetti Fernández Mc, Iannacone J, Hernández Contreras N, González Muñoz G, Poso del Sol MC, et al. Factores antropogénicos y ambientales sobre la fauna de culícidos (Diptera: Culicidae) de la provincia Sancti Spíritus, Cuba. The Biologist. 2015;13(1):53-74.
Rodríguez-Sosa MA, Rueda J, Vásquez-Bautista YE, Fimia-Duarte R, Borge-de Prada M, Guerrero KA, et al. Diversidad de mosquitos (Diptera: Culicidae) de Jarabacoa, República Dominicana. Graellsia. 2019;75(1):e084.
Osés-Rodríguez R, Burgos-Alemán I, Osés-Llanes C, Otero--Martin M, Fimia-Duarte R, Cruz-Camacho L. How much methodology ROR explains the rain errors in Caibarién, Cuba. International Journal of Current Advanced Research. 2015;4(2):17-21.
Osés-Rodríguez R, Fimia-Duarte R, Cruz-Camacho L. Simple ModeltoEstimate Longitude and Area of Universe. Open Access Library Journal.2015;2:e1334. http://doi.org/d5pf.
Osés-Rodríguez R, Fimia-Duarte R, Iannacone J, Saura-Gonzáles G, Gómez-Camacho L, Ruiz-Cabrera N. Modelación de la temperatura efectiva equivalente para la estación del Yabú y para la densidad larval total de mosquitos en Caibarién, provincia Villa Clara, Cuba. Rev Peruana Entomol. 2016;51(1.2):1-7.
Fimia-Duarte R, González-González R, Cepero-Rodríguez R, Valdés-Álvarez M, Osés-Rodríguez R, Corona-Santander E, et al. Influencia de algunas variables climáticas sobre la malacofauna fluvial con importancia zoonótica en la provincia Villa Clara. REDVET Revista Electrónica de Veterinaria. 2012 [cited 2020 Aug 3];13(7). Available from: https://bit.ly/33lLACy.
Alkhaldy I. Modelling the association of dengue fever cases with temperature and relative humidity in Jeddah, Saudi Arabia - a generalised linear model with break-point analysis. Acta Trop. 2017;168:9-15. http://doi.org/f9w77f.
García-Gutiérrez S, Pérez-Bastida JA, Fimia-Duarte R, Osés-Rodríguez R, Garín-Landa GM, González-González R. Influencia de algunas variables climatológicas sobre las densidades larvarias en criaderos de culícidos. Pol Cap. Roberto Fleites 2009-2010. REDVET Revista Electrónica de Veterinaria. 2012 [cited 2020 Aug 3];13(5). Available from: https://bit.ly/3k9Uc5q.
Fimia-Duarte R, Osés-Rodríguez R, Carmenate-Ramírez A, Iannacone J, González-González R, Gómez-Camacho L, et al. Modelación y predicción para moluscos con angiostrongilosis en la provincia Villa Clara, Cuba utilizando la regresión objetiva regresiva (ROR). Neotropical Helminthology. 2016;10(1):61-71.
Cómo citar
APA
ACM
ACS
ABNT
Chicago
Harvard
IEEE
MLA
Turabian
Vancouver
Descargar cita
CrossRef Cited-by
1. Sedthapong Laojun, Tanasak Changbunjong, Arina Abdulloh, Tanawat Chaiphongpachara. (2024). Geometric morphometrics to differentiate species and explore seasonal variation in three Mansonia species (Diptera: Culicidae) in central Thailand and their association with meteorological factors. Medical and Veterinary Entomology, 38(3), p.325. https://doi.org/10.1111/mve.12720.
Dimensions
PlumX
Visitas a la página del resumen del artículo
Descargas
Licencia
Derechos de autor 2020 Revista de la Facultad de Medicina

Esta obra está bajo una licencia Creative Commons Reconocimiento 3.0 Unported.
Derechos de autor
Los autores deben aceptar transferir a la Revista de la Facultad de Medicina los derechos de autor de los artículos publicados. La editorial tiene el derecho del uso, reproducción, transmisión, distribución y publicación en cualquier forma o medio. Los autores no podrán permitir o autorizar el uso de la contribución sin el consentimiento escrito de la revista. Estos archivos están disponibles en https://goo.gl/EfWPdX y https://goo.gl/6zztk4 y deben cargarse en el paso 4 del envío OJS (archivos complementarios).
La carta de cesión de derechos de autor y la de responsabilidad de autoría deben ser entregadas junto con el original.
Aquellos autores/as que tengan publicaciones con esta revista, aceptan los términos siguientes:
- Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
- Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista.
- Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) antes y durante el proceso de envío, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).