Humanity has suffered throughout history from the scourge of potentially fatal viral and parasitic diseases, including: Yellow Fever, Dengue, Zika, Chikungunya, Malaria, Chagas, Leishmaniasis, Onchocerciasis, Angiostrongylosis, Fasciolosis, among many others, and most of them often involve a vector organism as a common factor [1, 2]. These diseases are widespread in the tropics, with local variations in risk, so they are highly dependent on rainfall, temperature and rapid unplanned urbanization, among others [3-5] 

To these problems, we can now add global warming and the intensification of extreme meteorological disturbances, which has brought about changes in the behavior of diseases and their transmissions, with the establishment of vector species in places never recorded before [6-8]. Climate change is considered one of the main environmental problems, and its effects undoubtedly have a negative impact on human health [12, 9-11]. There are several studies that relate climatic variables with the increase of infectious diseases, where arboviruses has been one of the most studied, and it has been shown that there is a positive relationship between climate variation and the incidence of these infectious entities [12-16].

The relationship between the incidence of Dengue and climatological variables is mainly given in the characteristics of the vector, its life cycle and the conditions that favor the proliferation of the vector [17-20]. Among the most influential climatic variables, elevated temperature, humidity and precipitation volume are reported [21-24]. 

All vector-borne diseases in the world have very high incidence rates; for example, it is estimated that between 50-100 million cases of Dengue fever occur each year [4, 25, 26]. This viral entity has been considered for many years as a public health problem in the world, especially in tropical countries where the influences of environmental variables favor the increase of cases each year [4, 26, 27].

However, the main health problem in terms of vectors continues to be Malaria, with 500 million reported cases and three million deaths each year, of which one million are children under five years of age [27-29], this entity causes the death of one person every 60 seconds [29-32]. Cuba, due to its geographic location and climatological characteristics, has a wide fauna of culicidae with proven vectorial capacity, making them of great interest for human and other animal health [18, 32]. 

Efforts to control vector-borne diseases have been hampered in part by the development of drug-resistant etiologic agents, insecticide-resistant mosquitoes, environmental contamination, residual effect of chemicals, high prices of insecticides in the market and operational failures [28, 33-37].

There is the possibility of making forecasts of high quality, precision and certainty using several methodologies, among which stands out, the methodology of Regressive Objective Regression (ROR), which due to its simplicity and accuracy can open an important window to know the future of climate variables or daily data, years in advance [15, 22, 38]; this cycle can be extended to the 11 years of the solar cycle, or to higher cycles, which are known in nature; the population dynamics of mollusks and insects, such as culicidae and their interactions with certain environmental variables, can also be modeled, with the aim of establishing prophylactic and timely control measures in epidemiological surveillance programs [39-41]. Consequently, there is a growing need to develop and implement other strategies for the control of infectious entities and their vectors, which can complement existing methods in a more effective and efficient way. 

The objective of the research was to demonstrate the relationship between the transmissible infectious entities Dengue and Malaria with climate through mathematical modeling in Villa Clara province, Cuba.

Study Area:

The research was carried out in Villa Clara province, Cuba, whose provincial capital is Santa Clara municipality and covered the 13 municipalities that comprise it (Figure 1). Villa Clara province is located in the central region of the island of Cuba (Latitude: 22º 29'40'' N, Longitude: 79º28'30'' W), and has the following geographical limits; to the west, with Matanzas province, to the east, with Sancti Spíritus province and to the south, with Cienfuegos province (Figure 1). 

Figure 1: Political-administrative map of Cuba with the 13 municipalities of Villa Clara province

Period of Study and Data Collection:

The research covered the period from 2008 to 2020. Retrospective data on the main species of culicidae identified in the 304 permanent breeding sites and 218 temporary breeding sites in the province were collected from the existing control sheets/records kept in the Statistical Department of the Provincial Unit of Surveillance and Anti Vectorial Control (UPVLA) of Villa Clara province. The method used to collect mosquito larvae was the ladle method [42]. Likewise, the mosquito species to which the larvae belonged were identified by means of the MSB-9 stereo microscope, using specialized keys [43-45] and taking into account all recent changes in the systematics and taxonomy of Culicidae [46-48].

In the case of Dengue, a cross-sectional descriptive research was conducted in the province of Villa Clara, Cuba, from January 2017 to December 2020. The universe consisted of all patients who contracted Dengue fever during that period. The selected sample coincided with the total population under study. Monthly data corresponding to the number of Dengue cases in the province were used, and for the same period, a climatic database was created, which included variables from the Yabú meteorological station, located in Santa Clara municipality, capital of Villa Clara province. Both qualitative and quantitative variables were taken into account:

• N of monthly cases

• Incidence by municipality

• Incidence rates by municipality

• Average temperature (T. media)

• Maximum temperature (T. max)

• Minimum Temperature (T. min)

• Average Humidity (Med. R.H. Med)

• Maximum Relative Humidity (RHx)

• Minimum Relative Humidity (RHn)

• Precipitation (Prec)

• Wind speed (Vmed)

• Station Atmospheric Pressure (Patm)

• Cloudiness (Nub)

• Saturation Deficit (Dsat)

• Water Vapor Tension (Tva)

The technique for obtaining and collecting data was based on the use of the "Panorama of Diseases" register, which is obligatory, as well as the Provincial Meteorological Center's record of observations during the study period. The information obtained from these records was entered into a database, which was processed in the SPSS software, version 25. The Pearson coefficient was calculated and the Chi-square test was applied; in addition, the cases of Dengue were modeled in the short and long term using the methodology of the Regressive Objective Regression (ROR), with climatic variables (minimum temperature and precipitation).

Determination of Time Series and Trends:

In addition to collecting data on the main species of culicidae identified in the province, data were collected on larval densities, both general and specific (Anopheline) in each of the 13 municipalities of the province during the aforementioned study period. These data were organized in the Windows Excel application by years and months; that is, three columns were placed: the first with the years, the second with the 12 months that make up each year and the third with the values of the General Larval Density (GLD) and Specific Larval Density (SLD). This procedure was done for each municipality in the province. After organizing the data, we proceeded to obtain the time series and trend for each of the aforementioned variables, which was reflected in the line figures prepared for all the municipalities. 

Development of Predictive Models for Culicidae Population Dynamics:

Objective Regressive Regression (ORR) modeling was used to develop the predictive model. The following were defined as response variables: General Larval Density (GLD) and Specific Larval Density (SLD), and as explanatory variables: meteorological variables: Maximum Relative Humidity (RHX), Mean Relative Humidity (MRH), Minimum Relative Humidity (MRH), Maximum Temperatures (TX), Mean Temperatures (MT), Minimum Temperatures (MT), Provincial Precipitation (Prec.), Cloudiness (Nub.), Average Wind Speed (VMV), Atmospheric Pressure (PA). Data for both response and explanatory variables are from the same time period (2008-2015). The data of the meteorological variables were requested to the Provincial Meteorological Center of Villa Clara and they come from the four Meteorological stations of Villa Clara province, located in the municipalities of Santa Clara, Manicaragua, Caibarién and Sagua La Grande. The data obtained were processed by means of Pearson correlations and Student's t-test, as a statistical significance test. The statistical package SPSS ver. 13. 

Objective Regressive Regression Methodology (ORR):

In this methodology, the dichotomous variables DS, DI and NoC must be created first, where: NoC: Number of cases of the base (its coefficient in the model represents the trend of the series). DS = 1, if NoC is odd; DI = 0, if NoC is even, and vice versa. DS represents a sawtooth function and DI this same function, but in inverted form, so that the variable to be modeled is trapped between these parameters and a large amount of variance is explained. 

Subsequently, the module corresponding to the Regression analysis of the statistical package SPSS version 19.0 (IBM Company) is executed, specifically the ENTER method where the predicted variable and the ERROR are obtained. Then, the autocorrelograms of the ERROR variable are obtained, paying attention to the maximum of the significant partial autocorrelations (PACF), and then the new variables are calculated, taking into account the significant Lag of the PACF. Finally, these variables are included in the new regression, regressed in a process of successive approximations until a white noise in the regression errors is obtained. In the case of atmospheric pressure, the lags of one year in advance were used.

To date, 43 species of mosquitoes distributed in 15 genera have been identified in Villa Clara province, being the best represented and distributed species Anopheles albimanus [49], Culex quinquefasciatus, Cx. nigripalpu, Gymnometopa mediovittata, Psorophora confinnis, Aedes aegypti and Ae. Albopictus (present in all 13 municipalities of this province), followed by Culex corniger, Ochlerotatus scapularis (Rondan, 1848) and Psorophora ciliata (in 12 of the 13 existing municipalities), all of which are shown in table 1. 

Table 1: Distribution of mosquito species identified by municipality

Legend: List of municipalities of Villa Clara province: 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. 

Of the 69 species of mosquitoes registered for Cuba [50], in Villa Clara, the number of identified species is 43/62.31 %, so that species was collected in all the fluvial ecosystems sampled, which evidenced the great ecological plasticity of the entomofauna of culicidae existing in our country, in spite of being an archipelago, which corroborates the results obtained by (García, A. I. 1977; & González, B. R. 1985) [51, 52].

The genera best represented and with a marked presence in the municipalities studied were Anopheles, Culex and Psorophora, while the species best represented and distributed in this province were An. albimanus, Cx. quinquefasciatus, Cx. nigripalpus, Gy. medio vittata, Ps. confinnis, Ae. aegypti and Ae. albopictus (present in all 13 municipalities of this province), followed by Culex corniger, Ochlerotatus scapularis and Psorophora ciliata (in 12 of the 13 existing municipalities), being the most common and best represented species in this investigation: An. albimanus, An. crucians, Cx. atratus, Cx. quinquefasciatus, Cx. Nigripalpus and Ps. confinnis, because they were distributed in almost all the ecosystems sampled, where they appeared in relatively high abundance, a fact that agrees with the results obtained by (Marquetti, F. M. 2006) [19], specifically for Cx. quinquefasciatus in the urban ecosystem; this result also confirms the criteria of (Mattingly, P. F. 1962; Scorza, J. V. 1972; & CA, C. P. et al., 2006) [49, 53, 54] in relation to the extraordinary adaptive capacity and high ecological plasticity of Cx. quinquefasciatus in the most diverse and possible habitats provided by man.

The fact that Ae. aegypti and Ae. Albopictus are gaining ground and space in Villa Clara province is notorious and relevant. These species are of high entomo epidemiological risk because of their involvement in several infectious entities [26, 55-57 ], among which Dengue, Yellow Fever, West Nile virus, Chikungunya and Zika virus stand out; but reality has shown us, that at present, these two species are practically present throughout the length and breadth of the national geography, expanding increasingly, colonizing an important number of breeding sites generated by human activity together with environmental variables [58], thus showing their high ecological plasticity and high capacity to adapt to the most dissimilar ecological niches [15, 19].

When mathematically modeling the influence of the meteorological variables under study on the Specific Larval Density (An. albimanus) in the hatcheries (Table 2), it was observed that the correlations between anopheline larval density and temperatures were positive and highly significant for minimum and mean temperatures, which indicates that as temperatures increase, specific larval densities also increase, results that coincide with those reached by (García, G. S. et al. 2012; & Beck-Johnson, L. M. et al., 2013) [12, 31], who state that temperatures rather than rainfall play an important role in increasing anopheline larval density and malaria transmission. Both maximum and minimum temperatures have a positive association with anopheline larval densities and could be used as a predictor of the rate of Anopheles spp. infestation [59].

Table 2: Modeling of temperature on Specific Larval Density (SLD)

   ** Pearson's correlation significant at the 0.01 level (bilateral) 

   * Pearson's correlation significant at the 0.05 level (bilateral) 

  Source: Statistical records of the Institute of Meteorology of Villa Clara.

The association between temperature and population dynamics of culicidae has been investigated in many studies, particularly in tropical and sub-tropical areas. The results of this study are consistent with previous studies in other regions of the world [60, 61]. 

The correlations between specific larval density with Relative Humidity were also positive and highly significant. Indicating that as relative humidity increases, larval density increases (Table 3). We agree with (Farajzadeh, M. et al., 2015) [62], who define relative humidity as a vital meteorological variable in the survival of mosquitoes of the genus Anopheles. These researchers further found relative humidity and temperature to be strong predictors of Anopheles mosquito abundance. (Wilke, A. B. B. et al., 2017) [20] found associations between mosquito abundance and meteorological variables. In addition, they consider that predictive models based on meteorological variables can provide important information on the population dynamics of culicidae. Average relative humidity is a suitable predictor of anopheline larval density, and thus malaria transmission [63, 64].

Table 3: Modeling of Relative Humidity on Larval Specific Density 

* Pearson's correlation significant at the 0.05 level (bilateral) 

** Pearson's correlation is significant at the 0.01 level (bilateral). 

Source: Statistical records of the Institute of Meteorology of Villa Clara

In Table 4, when correlating the climatological variables (precipitation, cloudiness and average wind speed), no correlation was observed between anopheline larval density with precipitation and cloudiness. Mean wind speed had a negative and highly significant correlation, indicating that as mean wind speed decreases, larval density increases. These findings are in agreement with those found by (Bezerra, J. M. et al., 2016) [65], who did not find a positive association between Anopheles spp. larval density and rainfall. The highest larval densities determined by them were found in the months of the rainy period. This can be explained by the fact that, in the rainy season, the frequently high levels of rainfall could modify the life cycle of the culicidae, since an excessive increase in water levels in the breeding sites could contribute to the immature forms of the mosquito escaping or dying, and not being able to complete their life cycle. In addition, we agree with (Adu-Prah, S., & Tetteh, E. K. 2015) [63] who, despite finding that there was no significant association between rainfall and larval densities, considers that there is evidence that rainfall is an important predictor of larval density.

Table 4: Modeling of rainfall, cloud cover and mean wind speed on anopheline larval density

** Correlation significant at the 0.01 level (bilateral).

Source: Statistical records of the Institute of Meteorology of Villa Clara.

Table 5 shows that atmospheric pressure is a good predictor of culicidae larval densities, since a positive and significant association was found between both variables. As well as the influence exerted by the North Atlantic anticyclone, which dictates the behavior of atmospheric pressure in Cuba

Table 5: Modeling of atmospheric pressure on General Larval Density (GLD) in the municipality of Corralillo

This finding can be explained by the fact that oxygen taken under partial or total gas pressures is only regulated by changes in spiracle movements. At reduced oxygen tensions, both the opening and the time the spiracles remain open increases. At low atmospheric pressures, the spiracles remain permanently open, so that dehydration may be the main cause of mortality at low atmospheric pressures [66]. 

This finding constitutes a really novel result of the present work, since atmospheric pressure is a meteorological variable scarcely studied in terms of the influence that it can exert on the larval densities of culicidae of entomo epidemiological importance, since most of the investigations reviewed at national and international level address in their models the following meteorological variables: temperature, relative humidity and precipitation; however, atmospheric pressure has remained a scarcely studied variable, perhaps because of the very narrow range in which its values fluctuate over time. In fact, we were forced to find a biological explanation for the influence of atmospheric pressure on the General Larval Density (GLD) of culicidae, and we found the article by (Galun, R., & Fraenkel, G. 1961) [66] who investigated the influence of atmospheric pressure on mosquito larval densities artificially, since they had to create a team to achieve this purpose. 

Effects of Some Meteorological Variables on Dengue Fever in Villa Clara Province

In relation to the calculation of Pearson's correlation coefficient and its statistical significance, between the incidence of Dengue cases and the climatological variables, it should be noted that there was a significant correlation (r=0.332; p=0.023) between this variable and the Minimum Temperature, as this increases, the cases of Dengue increase, and in the case of Atmospheric Pressure it was also significant (r=-0.317; p=0.030), since as this increases, the cases of Dengue decrease. Water Vapor Tension was significant (r=-0.298; p=0.042), as it increases, cases of Dengue also increase. The other variables were not significant (Table 6).

Table 6: Correlation of climatological variables with the incidence of Dengue cases. Villa Clara 2017-2020

Source: Provincial Meteorological Center and Overview of notifiable diseases. Villa Clara 2017-2020.

Regarding the relationship of climatological variables with the incidence of Dengue. It was observed that the distributions of confirmed cases increased in the summer months and decreased considerably in the winter months. The intervals in which the considerable increase in cases was observed also coincided with the cyclonic season in Cuba, so it is inferred that there is a relationship in terms of the meteorological determinants that determine this period in the national territory and the incidence of Dengue, which is consistent with results obtained by other researchers, both in Villa Clara province and in other locations in Cuba [18, 36, 67-69]. 

In relation to the geographical distribution, a higher incidence was identified in the municipality of Santa Clara, which corresponds to an urban area with a higher population density, so it should be considered that the urbanization factor has a positive influence on the number of reported cases, which is consistent with results obtained by other authors, both in Cuba and in countries of the American continent [7, 34, 40, 69, 70]. 

Dengue cases were modeled in the short term using the ROR methodology (Table 7) with the climatic variables (Tmin and Prec), the latter variable was used even though no significant relationship was found in the study, because it is widely used in the studies consulted, due to its demonstrated incidence in the vector's life cycle. This model explains 95.4% of the variance, with an error of 135 cases, the Durbin Watson statistic is close to 2, so we are in the presence of a valid model; there is no more information on the errors.

Table 7: Summary of the model using the ROR methodology

Model summary ^{c, d}

1. Predictors: Lag17Prec, Lag Total, Lag14 Total, DS, Lag2Total, Lag10 Total, DI, Lag1 Total, NoC, Lag1Tmin.

2. b. For regression through the origin (the model without intercept), R-squared measures the proportion of the variability in the dependent variable over the origin explained by the regression. This CANNOT be compared to R-squared for models that include intercept.

3.  Dependent variable: Total

4.  Linear regression through the origin

In relation to the analysis of climatological variables, there was concordance between the results of the study and others reviewed, in which temperature and relative humidity are variables with high correlation with the incidence of Dengue [15, 16, 22, 23, 24, 71] .

Temperature, humidity and wind directly influence the occurrence of Dengue outbreaks; high temperature with average humidity and low winds create conditions conducive to an increase in the intensity of disease transmission [12, 15, 22, ,24, 71]. The influence of environmental temperature on the Aedes spp mosquito and the transmission of the Dengue virus, has postulated that, due to the action of climate change, the mosquito has appeared and adapted in places where it was not frequent, allowing the viruses to spread uncontrolled in different regions, potentiated by climatic variables (precipitation and humidity), which influence the infestation of areas in tropical and subtropical regions, related to a greater availability of breeding sites, and a higher frequency of feeding under conditions of water stress, which is a modulating factor in the emergence of epidemics and increase virus transmission [15, 22,24, 69, 71]. The model was significant at 100 % (Table 8), with a Fisher's F of 20, significant at 100%

Table 8: Analysis of Variance of the model for short-term Dengue

a.Dependent variable: Total

b. Linear regression through the origin

c. Predictors: Lag17Prec, Lag8Total, Lag14Total, DS, Lag2Total, Lag10Total, DI, Lag1Total, NoC, Lag1Tmin.

d. This total sum of squares is not corrected for the constant because the constant is zero for regression through the origin.

The ROR model in question (Table 9) consists of the following variables: DS (sawtooth) and DI (inverted sawtooth), which are dichotomous variables and the number of Dengue cases in backward steps in 1, 2, 8, 8, 10 and 14 months (Lag1 Total, Lag2Total, Lag8 Total,Lag10 Total, lag14 Total), also depends, on the Tmin regressed on 1 month (Lag1Tmin), and the Prec regressed on 17 months (Lag17Prec), as this increases, the cases of Dengue increase, for example, when the precipitation is 100 mm, the number of cases of Dengue increases by 131 cases in the month, the trend was significant to increase in 41 cases. All variables were significant.

Table 9: ROR modeling for Dengue cases regressed over time with some climatic variables

a. Dependent variable: Total

b. Linear regression through the origin

Variations in the water temperature in which mosquito larvae develop influence their survival. Thus, the development of Ae. aegypti is reduced when the water temperature decreases or increases due to the physiological optimum range, which oscillates between 16 and 35 °C [10, 11, 71, 72]. The effect of climate change has been evaluated on different aspects of Ae. aegypti biology, where precipitation and temperature have been reported as factors that influence not only the population dynamics of this species, but also its ability to transmit different types of viruses [11, 19, 20, 21, 7]. 

Moreover, precipitation plays an important role as a predisposing factor for Dengue, because while temperature influences virus replication, precipitation is related to mosquito habitat [10, 11, 12, 20, 73]. Although the increase in temperatures generates that humans store more water, so that the vector can spread more easily to urban areas; when the La Niña phenomenon occurs, soil moisture and naturally accumulated water residues are conducive to the vector's reproductive cycle [3, 7,  41, 65, 69].

The inverse relationship between the incidence of Dengue and atmospheric pressure could be due to the fact that the higher the atmospheric pressure, the lower the probability of rainfall. On the other hand, the direct relationship of dependence between cloudiness and temperature with the increase of Dengue cases is due to the fact that the higher the  for the proliferation of the vector [19, 21, 41, 71].

A long-term model was performed, that is, with a lag of 12 months (1 year) to search in advance in the forecast, obtaining a model that explains 82.9 %, with an error of 306 cases, where Fisher's F was 3.6, significant at 95 % (Table 10).

Table 10: Long-term modeling on a 12-month basis

a. Dependent variable: Total

b. Linear regression through the origin

c. Predictors: Lag14Patm, Lag26Total, Lag20Total, DI, Lag14 Total, Lag13 Total, NoC,

Lag13 min.

d. This total sum of squares is not corrected for the constant because the constant is zero

for regression through the origin.

Below are the results of the long-term model. In red the predicted value for the year 2021, where an increase is expected in the month of February to decrease from March to July, then from August to December, the values will increase greatly (Table 11).

Table 11: Long-term modeling for the year 2021

1. Limited to the first 100 cases. 

Figure 2 shows the good coincidence of the model, despite the fact that we are in the presence of a model 1 year in advance, a significant increase in Dengue cases in the province is expected for the second half of the year, a matter that should be treated with caution, so taking preventive measures would be very beneficial

Figure 2: Long-term forecast for Dengue cases in Villa Clara, Cuba

The ROR methodology consists of several steps and allows not only to mathematically model the larval densities of mosquitoes, as well as the population dynamics of mollusks, but goes beyond (possibility of modeling infectious entities of different etiologies, such as HIV/AIDS, Cholera, Influenza, Acute Respiratory Infections (ARI), Acute Bronchial Asthma (CAAB), Fasciolosis, Angiostrongylosis and even, in the estimation of the length and area of the universe, monthly forecasting of precipitation and extreme temperatures, forecasting of meteorological disturbances (hurricanes), prediction of the latitude and longitude of earthquakes, search for information on white noise, modeling of the Equivalent Effective Temperature (TEE) and Atmospheric Pressure (PA) up to the electricity consumption of a municipality, province and/or nation) [74-76].

It is concluded that there is a close relationship between the infectious entities analyzed and the species of vector organisms with the climate, demonstrated by means of the predictive model for the specific and general larval densities of culicidae, and the mathematical modeling ROR for the Dengue entity, so that it is possible to model and predict, Thus, it is possible to model and predict, in the short, medium and long term, both the population dynamics of culicidae with entomo epidemiological importance and the incidence of cases of Dengue, with very good results, making the ROR methodology an excellent predictive tool not only for transmissible entities of viral and parasitic etiology.

No funding sources.

None declared.

The study was approved by the Institutional Ethics Committee of University of Medical Sciences of Villa Clara.

1. Guzmán, Maria G., and Gustavo Kouri. "Dengue: an update." The Lancet infectious diseases 2.1 (2002): 33-42. https://doi.org/10.1016/S1473-3099(01)00171-2

2. (WHO) World Health Organization. Dengue and severe dengue. Descriptive note. (2019). Available in:  https://www.who.int/es/news-room/fact-sheets/detail/dengue-and-severedengue  

3. Troyo, Adriana, et al. "Seasonal profiles of Aedes aegypti (Diptera: Culicidae) larval habitats in an urban area of Costa Rica with a history of mosquito control." Journal of vector ecology: journal of the Society for Vector Ecology 33.1 (2008): 76. https://doi.org/10.3376%2F1081-1710(2008)33%5B76%3Aspoaad%5D2.0.co%3B2 

4. Gould, Ernest, et al. "Emerging arboviruses: Why today?." One health 4 (2017): 1-13. https://doi.org/10.1016/j.onehlt.2017.06.001 

5. Benavides, M. J. A., et al. "Sociodemographic and clinical characterization of patients diagnosed with dengue and chikungunya in Nariño, Colombia." Cuban J Trop Med 73.1 (2021): e451.

6. Gore, A. "An inconvenient truth [videocinta] EUA: Paramount Classic and Participant Productions." (2007).

7. Fajardo-Herrera, Reinaldo José, Juan-Carlos Valdelamar-Villegas, and Darlis Arrieta-Pérez. "Prediction of the potential establishment of the mosquito Aedes aegypti in non-residential urban spaces in Colombia using eco-urban and landscape variables." Gestión y Ambiente 20.1 (2017): 95. https://search.proquest.com/openview/2e6a9f22e48ca1dfd907e9a6df11308d/1?pq-origsite=gscholar&cbl=2035730 

8. Real-Cotto, J. J. "Factors related to the dynamics of dengue in Guayaquil based on historical trends." (2017): 23-28. http://revistasinvestigacion.unmsm.edu.pe/index.php/anales/article/view/13017/11726 

9. Ehelepola, N. D. B., et al. "A study of the correlation between dengue and weather in Kandy City, Sri Lanka (2003-2012) and lessons learned." Infectious diseases of poverty 4 (2015): 1-15. https://link.springer.com/article/10.1186/s40249-015-0075-8 

10. Sharmin, Sifat, et al. "Interaction of mean temperature and daily fluctuation influences dengue incidence in Dhaka, Bangladesh." PLoS neglected tropical diseases 9.7 (2015): e0003901. https://doi.org/10.1371/journal.pntd.0003901 

11. Márquez, M. K., et al. "Influence of environmental temperature on the Aedes spp mosquito and Dengue virus transmission." CES Medicine 33.1 (2019): 18-23.

12. García, G. S., et al. "Influence of some climatological variables on larval densities in culicidae hatcheries. Pol Cap. Roberto Fleites 2009-2010." REDVET 13.05B (2012): 1-9. URL: http://www.veterinaria.org/revistas/redvet/n050512B.html

13. Osés-Rodríguez, R., et al. "Modelación matemática del cólera por medio de la Regresión Objetiva Regresiva y su relación con las variables climáticas. Caibarién, Villa Clara, Cuba." Abstract Book del VI Congreso Internacional de Parasitología Neotropical (VI COPANEO)“Impacto del Cambio Climático en las Enfermedades Parasitarias”. Lima: The Biologis. 2017.

14. Sánchez, Á., et al. "Objective Regressive Regression beyond white noise for viruses circulating in Villa Clara province, Cuba." The Biologist (Lima) 15 (2017): 127.

15. Osés, R. R., et al. "Incidence of the annual rhythm in some climatic variables on culicidae larval populations: forecast for the 2018 cyclonic season in Villa Clara, Cuba." The Biologist (Lima) 16 (2018). Disponible en: http://sisbib.unmsm.edu.pe/BVRevistas/biologist/biologist.htm

16. Fimia, D. R., et al. "Mathematical modeling of the effect of atmospheric pressure on the population density of mosquitoes (Diptera: Culicidae) in Villa Clara, Cuba." Journal of the School of Medicine (Rev. Fac. Med.) 68.4 (2020): 541-549. URL: http://www.eduvim.com.ar/coedicion/colombia   

17. Beerntsen, Brenda T., Anthony A. James, and Bruce M. Christensen. "Genetics of mosquito vector competence." Microbiology and molecular biology reviews 64.1 (2000): 115-137. https://doi.org/10.1128/mmbr.64.1.115-137.2000 

18.  Fernández, María del Carmen Marquetti, and Juán Andrés Bisset Lazcano. Aspectos bioecológicos de importancia para el control de Aedes aegypti y otros culícidos en el ecosistema urbano. Editorial Universitaria, 2008. https://tesis.sld.cu/index.php?P=DownloadFile&Id=99 

19. Fimia Duarte, R., et al. "Anthropogenic and environmental factors on culicid fauna (Diptera: Culicidae) of Sancti Spíritus province, Cuba." (2015): 53-74. http://sisbib.unmsm.edu.pe/bvrevistas/biologist/v13_n1/pdf/a06v13n1.pdf 

20. Wilke, André Barretto Bruno, et al. "Mosquito populations dynamics associated with climate variations." Acta tropica 166 (2017): 343-350.  URL: http://dx.doi.org/10.1016/j.actatropica.2016.10.025

21. Osés, R. R., et al. "Modeling of the equivalent effective temperature for the Yabu season and for the total larval density of mosquitoes in Caibarién, Villa Clara province, Cuba." Peruvian Journal of Entomology 51.1 (2016): 1-7.

22. DuarteP, Rigoberto Fimia, and José IannaconeP. "Mathematical modeling and climate: incidence, repercussion and impact on communicable entities and vector organisms." Himalayan Journal of Applied Medical Sciences and Research 3.4 (2022). URL: http://www.veterinaria.org/revistas/redvet , http://www.veterinaria.org/revistas/redvet/n060618/061804.pdf   

23. Fimia, D. R., et al. "Mathematical modeling of the population dynamics of the Aedes aegypti mosquito (Diptera: Culicidae) with some climatic variables in Villa Clara, Cuba. 2007-2017." The Biologist (Lima) 17.2 (2019): 1-8.

24. Fimia, D. R., et al. "Mathematical modeling of population dynamics of the Aedes aegypti (Diptera: Culicidae) mosquito with some climatic variables in Villa Clara, Cuba." International Journal of Zoology and Animal Biology (IZAB) 3.3 (2020): 1-3. URL:  https://medwinpublishers.com/IZAB/ 

25. Ortega, Luis G. "Dengue: An Ever-Emerging Problem." Epidemiology, vol. 14, no. 2, (2001), pp. 41-52. URL: http://www.sld.cu/sitios/desastres/temas.php?idv=14159

26. Guzman, Maria G., Mayling Alvarez, and Scott B. Halstead. "Secondary infection as a risk factor for dengue hemorrhagic fever/dengue shock syndrome: an historical perspective and role of antibody-dependent enhancement of infection." Archives of virology 158 (2013): 1445-1459. https://link.springer.com/article/10.1007/s00705-013-1645-3 

27. Service, M. W. "Importance of ecology in Aedes aegypti control." The Southeast Asian journal of tropical medicine and public health 23.4 (1992): 681-690. https://pubmed.ncbi.nlm.nih.gov/1298074/ 

28. Das, P. K., and D. Dominic Amalraj. "Biological control of malaria vectors." The Indian journal of medical research 106 (1997): 174-197. https://europepmc.org/article/med/9291686 

29. Dia, Ibrahima, et al. "Bionomics of Anopheles gambiae Giles, An. arabiensis Patton, An. funestus Giles and An. nili (Theobald)(Diptera: Culicidae) and transmission of Plasmodium falciparum in a Sudano-Guinean zone (Ngari, Senegal)." Journal of medical entomology 40.3 (2003): 279-283. https://doi.org/10.1603/0022-2585-40.3.279 

30. Póvoa, Marinete M., et al. "Malaria vectors, epidemiology, and the re-emergence of Anopheles darlingi in Belém, Pará, Brazil." Journal of medical entomology 40.4 (2003): 379-386. https://doi.org/10.1603/0022-2585-40.4.379 

31. Beck-Johnson, Lindsay M., et al. "The effect of temperature on Anopheles mosquito population dynamics and the potential for malaria transmission." PLOS one 8.11 (2013): e79276. https://doi.org/10.1371/journal.pone.0079276 

32. Fimia Duarte, Rigoberto, et al. "Modeling of Equivalent Effective Temperature and its possible incidence on larval density of Anopheles mosquitoes (Diptera: Culicidae) in Villa Clara province, Cuba." Revista de Biología Tropical 65.2 (2017): 565-573. https://www.scielo.sa.cr/scielo.php?pid=S0034-77442017000200565&script=sci_arttext&tlng=en 

33. MG, Guzmán Tirado. "Emergence of dengue hemorrhagic fever in the Americas. Reemergence of dengue." Revista Cubana de Medicina Tropical 51.1 (1999): 5-13. https://europepmc.org/article/med/10887549 

34. Ayala, S. Y. O., et al. "Behavioral response of Aedes aegypti (Linnaeus, 1762) to pyrethroid adulticides commonly used in public health." Peruvian Journal of Experimental Medicine and Public Health 25.1 (2008): 26-34.  URL: http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S1726-46342008000100005

35. Ngoagouni, Carine, et al. "Invasion of Aedes albopictus (Diptera: Culicidae) into central Africa: what consequences for emerging diseases?." Parasites & Vectors 8 (2015): 1-7. https://link.springer.com/article/10.1186/s13071-015-0808-3 

36. Fimia-Duarte, R., et al. "Mosquitoes (Diptera: Culicidae) and their control by means of biological agents in Villa Clara province, Cuba." International Journal of Current Research 8.12 (2016): 43114-20.

37. Alarcón-Elbal, P. M., et al. "Arboviral diseases spread by mosquitoes (Diptera: Culicidae) in the Dominican Republic: a review." (2017): 193-219. http://revistas.unfv.edu.pe/index.php/rtb/article/view/155/147 

38. Osés-Rodríguez, R. I. C. A. R. D. O., et al. "The ROR's methodology an it's possibility to find information in a white noise." International Journal of Current Research 9.3 (2017): 47378-82.

39. Fimia, D. R., et al. "Modeling and prediction up to 2020 for total angiostrongylosis using Objective Regression Regression. Villa Clara, Cuba." The Biologist (Lima) 15.1 (2017): S16-S16.

40. González-Rizo, Aileen, et al. "Effect of chlorine and temperature on larvicidal activity of cuban bacillus thuringiensis isolates." Journal of Arthropod-Borne Diseases 13.1 (2019): 39. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6643015/ 

41. Machado, V. I., et al. "The resident focal point: an alternative in the control of Aedes aegypti (Diptera: Culicidae) and the prevention of dengue in a health area of Santa Clara municipality, Villa Clara, Cuba." REDVET 19.6 (2018): 1-7. URL: http://www.veterinaria.org/revistas/redvet , http://www.veterinaria.org/revistas/redvet/n060618/061806.pdf

42. (WHO) World Health Organization. Biological control of disease vectors. Technical Report Series, (1982). 679. Geneva, Switzerland.

43. Perez, Vicente I. The Ixodid and Culicidae of Cuba: Its Natural and Medical History. University of Havana, (1956), Havana, 580 pages.

44. Reus, Jorge Méndez, et al. "Clave pictórica para identificar géneros de mosquitos cubanos en su etapa larval." REDVET. Revista Electrónica de Veterinaria 13.5 (2012): 1-16. https://www.redalyc.org/pdf/636/63624365023.pdf 

45. González, Ramón González. "La entomofauna de culícidos y los copépodos abordados desde las alternativas de control biológico hasta la modelación matemá-tica en dos provincias centrales de Cuba." Anales de la Academia de Ciencias de Cuba; Vol 10.3 (2020). https://www.researchgate.net/profile/Rigoberto-Fimia-Duarte/publication/345120634_CIENCIAS_BIOMEDICAS/links/5f9dcb74a6fdccfd7b907be9/CIENCIAS-BIOMEDICAS.pdf 

46. Reinert, John F. "New classification for the composite genus Aedes (Diptera: Culicidae: Aedini), elevation of subgenus Ochlerotatus to generic rank, reclassification of the other subgenera, and notes on certain subgenera and species." Journal of the American Mosquito Control Association-Mosquito News 16.3 (2000): 175-188. https://mosquito-taxonomic-inventory.myspecies.info/sites/mosquito-taxonomic-inventory.info/files/Reinert%202000_1.pdf 

47. Reinert, John F. "Revised list of abbreviations for genera and subgenera of Culicidae (Diptera) and notes on generic and subgeneric changes." Journal of the American Mosquito Control Association-Mosquito News 17.1 (2001): 51-55. https://www.biodiversitylibrary.org/content/part/JAMCA/JAMCA_V17_N1_P051-055.pdf 

48. Reinert, John F., Ralph E. Harbach, and Ian J. Kitching. "Phylogeny and classification of Aedini (Diptera: Culicidae), based on morphological characters of all life stages." Zoological Journal of the Linnean Society 142.3 (2004): 289-368. https://doi.org/10.1111/j.1096-3642.2004.00144.x 

49. Scorza, José V. Observaciones bionómicas sobre Culex pipiens fatigans Wied., 1829 de Venezuela. Ediciones del Rectorado, 1972.

50. Gonzalez, R., "Description of a new species of Culex (Culex)(Diptera: Culicidae) from Cuba." Bulletin of the Aragonese Entomological Society (SEA), vol. 52, no. 4, (2013), pp. 117-119.

51. Top of Form

52. García, Antonio I. Cuban Fauna of Mosquitoes and Their Typical Breeding Places. 1st ed., Cuban Academy of Sciences, (1977), Havana.

53. González, B. R. "New reports on the tribe Sabethini (Diptera: Culicidae) for Cuba." (1985): 1-11.

54. Mattingly, P. F. "The urban mosquito hazard today." Bulletin of the World Health Organization 29.Suppl (1963): 135. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2554876/ 

55. CA, Cruz Pineda, María Virginia, and Cabrera Carmenate. "Entomological-ecological characterization of positive cases and suspected cases of West Nile virus in Sancti Spiritus province, Cuba." Revista Cubana de Medicina Tropical 58.3 (2006): 235-240. https://europepmc.org/article/med/23424792 

56. Komar, Nicholas. "West Nile virus: epidemiology and ecology in North America." Advances in virus research 61 (2003): 185-234. https://books.google.com/books?hl=en&lr=&id=TKm5NCds8gEC&oi=fnd&pg=PA185&dq=55.%09Komar,+N.+(2003).+West+Nile+virus:+epidemiology+and+ecology+in+North+America.+Advances+in+virus+research,+61,+185-234.&ots=_OKgXumh1_&sig=nVpbcaDOx9ykPkRSQvSqadADbTQ 

57. Mackenzie, John S., Duane J. Gubler, and Lyle R. Petersen. "Emerging flaviviruses: the spread and resurgence of Japanese encephalitis, West Nile and dengue viruses." Nature medicine 10.Suppl 12 (2004): S98-S109. https://www.nature.com/articles/nm1144 

58. Pupo, A. M., et al. "Serological study in localities with confirmed West Nile virus infections." Revista Cubana de Medicina Tropical, vol. 63, no. 3, (2011), pp. 227-230. 

59. Bangs, Michael J., et al. "Climatic factors associated with epidemic dengue in Palembang, Indonesia: implications of short-term meteorological events on virus transmission." Southeast Asian Journal of Tropical Medicine and Public Health 37.6 (2006): 1103. https://www.thaiscience.info/Journals/Article/TMPH/10472371.pdf 

60. Zhang, Ying, Peng Bi, and Janet E. Hiller. "Meteorological variables and malaria in a Chinese temperate city: A twenty-year time-series data analysis." Environment international 36.5 (2010): 439-445. https://doi.org/10.1016/j.envint.2010.03.005 

61. Zhou, Guofa, et al. "Association between climate variability and malaria epidemics in the East African highlands." Proceedings of the National Academy of Sciences 101.8 (2004): 2375-2380. https://www.pnas.org/doi/abs/10.1073/pnas.0308714100 

62. Zhang, Yi, et al. "The impact of temperature and humidity measures on influenza A (H7N9) outbreaks—evidence from China." International Journal of Infectious Diseases 30 (2015): 122-124. https://doi.org/10.1016/j.ijid.2014.11.010 

63. Farajzadeh, Manuchehr, et al. "Spatiotemporal Anopheles Population Dynamics, Response to Climatic Conditions: The Case of Chabahar, South Baluchistan, Iran." Annals of global health 81.5 (2015): 694-704. URL: http://dx.doi.org/10.1016/j.aogh.2015.12.003

64. Adu-Prah, Samuel, and Emmanuel Kofi Tetteh. "Spatiotemporal analysis of climate variability impacts on malaria prevalence in Ghana." Applied Geography 60 (2015): 266-273. URL: http://dx.doi.org/10.1016/j.apgeog.2014.10.010

65. Alkhaldy, Ibrahim. "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 tropica 168 (2017): 9-15. URL: http://dx.doi.org/10.1016/j.actatropica.2016.12.034

66. Bezerra, Juliana MT, et al. "Aedes (Stegomyia) albopictus’ dynamics influenced by spatiotemporal characteristics in a Brazilian dengue-endemic risk city." Acta tropica 164 (2016): 431-437. URL: http://dx.doi.org/10.1016/j.actatropica.2016.10.010

67. Galun, Rachel, and G. Fraenkel. "The effect of low atmospheric pressure on adult Aedes aegypti and on housefly pupae." Journal of Insect Physiology 7.3-4 (1961): 161-176. https://doi.org/10.1016/0022-1910(61)90069-5 

68. Rodríguez, M. J., O. Cepero, and A. Rodríguez. "Vigilancia y control en criaderos temporales y permanentes de culícidos en Villa Clara." REDVET 7.7 (2006): 12-6. URL: http://www.veterinaria.org/

69. Fimia Duarte, R., et al. "Control of mosquitoes (Diptera: Culicidae) using biomathematical methods in Villa Clara province." (2012): 031206. URL: http://www.veterinaria.org/revistas/redvet/n030312/031206

70. Diéguez-Fernández, L., et al. "Seasonal behavior and relevance of permanent and usable containers to the presence of Aedes (Stegomyia) aegypti in Camagüey, Cuba." (2015): 103-111.  URL: http://aphiaperu.com/volumenes.htm

71. Rydzanicz, Katarzyna, et al. "Assessment of mosquito larvae (Diptera: Culicidae) productivity in urban cemeteries in Wroclaw (SW Poland)." International Journal of Tropical Insect Science 41.4 (2021): 3249-3255.  URL: https://doi.org/10.1007/s42690-020-00415-1 

72. Fimia, D. R., et al. "Mathematical modeling of the population dynamics of the Aedes aegypti mosquito (Diptera: Culicidae) with some climatic variables in Villa Clara, Cuba. 2007-2017." The Biologist (Lima) 17.2 (2019): 1-8. URL: http://sisbib.unmsm.edu.pe/BVRevistas/biologist/biologist.htm

73. Benítez, P. M. O. "Role of Aedes mosquitoes in pathogen transmission." AMC 22.5 (2018): 634-639. URL: http://scielo.sld.cu/scielo.php?script=sci_arttext&pid=S1025-02552018000500634&lng=pt

74. Xu, Hai-Yan, et al. "Statistical modeling reveals the effect of absolute humidity on dengue in Singapore." PLoS neglected tropical diseases 8.5 (2014): e2805. https://doi.org/10.1371/journal.pntd.0002805 

75. Oses, R., and R. Grau. "Regressive modeling (ROR), versus ARIMA modeling, using dichotomous variables in HIV mutations." Marta Abreu de las Villas Central University. Feijóo (2011).

76. Osés Rodríguez, R., A. Carmenate Ramírez, and F. A. Pedraza Martinez. "Prediction of the latitude and longitude of earthquakes at global level using the regressive objective regression." REDVET 19.5 (2018). DOI: doi.org/10.33140/ATCP 

77. Osés, Roberto R., et al. "Study of the provincial electricity consumption of Villa Clara and its forecast 2019-2023 Cuba." ECOSOLAR Magazine, vol. 65, (2019), pp. 32-43. Disponible en: http://www.cubasolar.cu/biblioteca/ecosolar