One of the barriers to performing geospatial surveillance of mosquito occupancy or infestation anywhere in the world is the paucity of primary entomologic survey data geolocated at a residential property level and matched to important risk factor information (e.g., anthropogenic, environmental, and climate) that enables the spatial risk prediction of mosquito occupancy or infestation. Such data are invaluable pieces of information for academics, policy makers, and public health program managers operating in low-resource settings in Africa, Latin America, and Southeast Asia, where mosquitoes are typically endemic. The reality is that such data remain elusive in these low-resource settings and, where available, high-quality data that include both individual and spatial characteristics to inform the geospatial description and risk patterning of infestation remain rare. There are many online sources of open-source spatial data that are reliable and can be used to address such data paucity in this context. Therefore, the aims of this article are threefold: (1) to highlight where these reliable open-source data can be acquired and how they can be used as risk factors for making spatial predictions for mosquito occupancy in general; (2) to use Brazil as a case study to demonstrate how these datasets can be combined to predict the presence of arboviruses through the use of ecological niche modeling using the maximum entropy algorithm; and (3) to discuss the benefits of using bespoke applications beyond these open-source online data sources, demonstrating for how they can be the new “gold-standard” approach for gathering primary entomologic survey data. The scope of this article was mainly limited to a Brazilian context because it builds on an existing partnership with academics and stakeholders from environmental surveillance agencies in the states of Pernambuco and Paraiba. The analysis presented in this article was also limited to a specific mosquito species, i.e., Aedes aegypti , due to its endemic status in Brazil.

Dengue is considered one of the biggest public health problems in recent decades. Climate and demographic changes, the disorderly growth of cities and international trade have brought new arboviruses such as chikungunya and Zika. Control of arboviruses depends on control of the vector: the Aedes aegypti mosquito. In this work, we propose a methodology for building disease predictors capable of predicting infected cases and locations based on machine learning. We also propose an artificial experts committee based on meta-heuristic methods to detect the most relevant risk factors. Method As a case study, we applied the methodology to forecast dengue, chikungunya and Zika, with data from the City of Recife, Brazil, from 2013 to 2016. We used arboviruses cases data and climatic and environmental information: wind speeds, temperatures and precipitation. Results The best prediction results were obtained with 10-tree Random Forest regression, with Pearson’s correlation above 0.99 and RMSE (%) below 6%. Additionally, the artificial experts committee was able to present the most relevant factors for predicting cases in each two-month period. The spatiotemporal prediction results showed the evolution of arboviruses, pointing out as major focuses on both regions richer in urban green areas and low-income neighborhood with irregular water supply. Determining the most relevant factors for prediction, as well as the spatial distribution of cases, can be useful for the planning and execution of public policies aimed at improving the health infrastructure and planning and controlling the vector.

Abstract Purpose: Aedes aegypti is a mosquito responsible for transmitting mainly dengue, zika, and chikungunya. In low- and middle-income countries, controlling the spread of this mosquito poses a major public health challenge. Currently, Aedes aegypti control policies are extremely important for lowering the risk of potential arbovirus outbreaks. One of the effective strategies for combating the burden of mosquito-borne arboviruses are the pre-emptive predictions and forecasts for future outbreaks. In this sense, we, therefore, apply machine learning using a spatiotemporal approach to build distribution maps of Aedes aegypti breeding sites in Recife. Methods: We obtained data from Aedes aegypti breeding sites and climatic factors in Recife City during 2013-2014. From the information of the breeding sites, bimonthly spatial distribution maps of the breeding sites were generated using the Inverse Distance Interpolation (IDW). We generated monthly spatial distribution maps of climatic variables (temperature, rainfall, and wind speed) using the same method. From the generated distribution maps, several models were evaluated, among them: support vector regressor, multilayer perceptron, random forest, and linear regression. The model performance was evaluated according to Pearson's correlation coefficient and percentage root relative squared error (RRSE%) metrics. Results: Among the evaluated regressors, the 3-degree polynomial-kernel support vector regressor showed superior performance compared to the other regressors evaluated. For this regressor, the correlation coefficient was on average 0.9875 (and standard deviation of 0.01) while the RRSE% metric was on average 14.60%. Conclusion: Machine learning proved to be a promising tool in predicting the Aedes aegypti breeding sites’ spatial distribution in the city of Recife. The spatiotemporal predictions pointed out that neighborhoods with low income and lack of water supply are presented with elevated concentrations of mosquito breeding sites. The findings of this work can support health authorities in decision-making linked to policies for reducing the burden of mosquito infestation, while at the same time allowing authorities to optimize their limited resources in low-resource settings.

Arboviruses are a group of diseases that are transmitted by an arthropod vector. Since they are part of the Neglected Tropical Diseases that pose several public health challenges for countries around the world. The arboviruses' dynamics are governed by a combination of climatic, environmental, and human mobility factors. Arboviruses prediction models can be a support tool for decision-making by public health agents. In this study, we propose a systematic literature review to identify arboviruses prediction models, as well as models for their transmitter vector dynamics. To carry out this review, we searched reputable scientific bases such as IEE Xplore, PubMed, Science Direct, Springer Link, and Scopus. We search for studies published between the years 2015 and 2020, using a search string. A total of 429 articles were returned, however, after filtering by exclusion and inclusion criteria, 139 were included. Through this systematic review, it was possible to identify the challenges present in the construction of arboviruses prediction models, as well as the existing gap in the construction of spatiotemporal models.

Certain weather conditions are inadvertently related to increased population of various mosquitoes. In order to predict the burden of mosquito populations in the Global South, it is imperative to integrate weather-related risk factors into such predictive models. There are a lot of online open-source weather platforms that provide historical, current and future weather forecasts which can be utilised for general predictions, and these electronic sources serve as an alternate option for weather data when physical weather stations are inaccessible (or inactive). Before using data from such online source, it is important to assess the accuracy against some baseline measure. In this paper, we therefore evaluated the accuracy and suitability of weather forecasts of two parameters namely temperature and humidity from the OpenWeatherMap API (an online weather platform) and compared them with actual measurements collected from the Brazilian weather stations (INMET). The evaluation was focused on two Brazilian cites, namely, Recife and Campina Grande. The intention is to prepare an early warning model which will harness data from OpenWeatherMap API for mosquito prediction.

Dengue has become a challenge for many countries. Arboviruses transmitted by Aedes aegypti spread rapidly over the last decades. The emergence chikungunya fever and zika in South America poses new challenges to vector monitoring and control. This situation got worse from 2015 and 2016, with the rapid spread of chikungunya, causing fever and muscle weakness, and Zika virus, related to cases of microcephaly in newborns and the occurrence of Guillain-Barret syndrome, an autoimmune disease that affects the nervous system. The objective of this work was to construct a tool to forecast the distribution of arboviruses transmitted by the mosquito Aedes aegypti by implementing dengue, zika and chikungunya transmission predictors based on machine learning, focused on multilayer perceptrons neural networks, support vector machines and linear regression models. As a case study, we investigated forecasting models to predict the spatio-temporal distribution of cases from primary health notification data and climate variables (wind velocity, temperature and pluviometry) from Recife, Brazil, from 2013 to 2016, including 2015’s outbreak. The use of spatio-temporal analysis over multilayer perceptrons and support vector machines results proved to be very effective in predicting the distribution of arbovirus cases. The models indicate that the southern and western regions of Recife were very susceptible to outbreaks in the period under investigation. The proposed approach could be useful to support health managers and epidemiologists to prevent outbreaks of arboviruses transmitted by Aedes aegypti and promote public policies for health promotion and sanitation.

Arboviruses are diseases transmitted by viruses which are maintained in the wild through a vertebrate host and a hematophagous arthropod, such as a mosquito. The transmitter vector of an arbovirus is the arthropod which transmits the virus from one vertebrate to the other through a bite. The biological transmission usually occurs when the hematophagous arthropod feeds on a viremic vertebrate and deposits infectious saliva during the feeding of the blood of another vertebrate. However, in some types of arboviruses, the biological transmission occurs directly in the human–mosquito cycle. Moreover, other forms of transmission have been reported, such as transmission from mother to child during pregnancy, blood transfusion, and through sexual intercourse. Demographic changes and the intense migratory flow from rural areas to urban areas have generated disorderly growth in cities. Deficiencies in basic sanitation also contribute to the vector's proliferation in tropical and subtropical countries. Brazil, which is a tropical country, is very affected by arboviruses, such as dengue, malaria, and yellow fever. With climate change and the increase in the number and frequency of international flights, two new arboviruses transmitted by the Aedes aegypti mosquito appeared in Brazil: the chikungunya and the Zika virus. This situation brings new challenges regarding the control and vector monitoring. The advancement of Digital Epidemiology, together with the development of Data Mining and Machine Learning techniques, provided rapid monitoring, control, and simulation of the spread of diseases. With this in mind, the prediction tools are able to assist public health systems in controlling epidemics and behavioral factors that favor the vector of these diseases. In this sense, in this chapter we present a literature review to identify methods of predicting cases of arboviruses, as well as the prediction of breeding sites.

This paper explores the main factors for mosquito-borne transmission of the Zika virus by focusing on environmental, anthropogenic, and social risks. A literature review was conducted bringing together related information from this genre of research from peer-reviewed publications. It was observed that environmental conditions, especially precipitation, humidity, and temperature, played a role in the transmission. Furthermore, anthropogenic factors including sanitation, urbanization, and environmental pollution promote the transmission by affecting the mosquito density. In addition, socioeconomic factors such as poverty as well as social inequality and low-quality housing have also an impact since these are social factors that limit access to certain facilities or infrastructure which, in turn, promote transmission when absent (e.g., piped water and screened windows). Finally, the paper presents short-, mid-, and long-term preventative solutions together with future perspectives. This is the first review exploring the effects of anthropogenic aspects on Zika transmission with a special emphasis in Brazil.