Geospatial analysis of Aedes poicilius, vector of Bancroftian Filariasis in the Philippines.

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Learn more: PMC Disclaimer | PMC Copyright Notice J Parasit Dis . 2024 Dec 6;49(2):407–418. doi: 10.1007/s12639-024-01766-z Geospatial analysis of Aedes poicilius , vector of Bancroftian Filariasis in the Philippines Loida M Recopuerto-Medina 1 , Regina Rebecca A Atienza 1 , Jorienne Mae F Flaminiano 1 , Margaret M Manuel 1 , Gia Mikaela T Perez 1 , Corky Philip M Zeta 1 , Francis Kristin Nicole R Medina 2 , Nikki Heherson A Dagamac 1, 3, 4, ✉ Author information Article notes Copyright and License information 1 Department of Biological Sciences, College of Science, University of Santo Tomas, 1008 Espana, Manila, Philippines 2 College of Nursing, University of Santo Tomas, 1008 Espana, Manila, Philippines 3 The Graduate School, University of Santo Tomas, 1008 Espana, Manila, Philippines 4 Research Center for the Natural and Applied Sciences, University of Santo Tomas, 1008 Espana, Manila, Philippines ✉ Corresponding author. Received 2024 Mar 28; Accepted 2024 Nov 20; Issue date 2025 Jun. © Indian Society for Parasitology 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. PMC Copyright notice PMCID: PMC12126396  PMID: 40458515 Abstract Bancroftian filariasis, one of the Philippines’ neglected tropical diseases, is a parasitic disease caused by Wuchereria bancrofti and transmitted by Aedes poicilius , which thrive in the Musa plantations abundant in certain Philippine regions. Eliminating this disease is far from being achieved, thus emphasizing the need for a better control or elimination program by constructing a contemporary predictive model of the mosquito, A. poicilius , and identifying key environmental variables that favor the mosquito species. Modeling of the distribution of lymphatic filariasis was divided into two phases: data collection of disease occurrences and environmental variables from 1985 to 2019 and model calibration and testing utilizing the MaxEnt algorithm. Model sensitivity was validated through the area under the curve (AUC) method. The model had a mean training AUC of 0.995 ± 0.001. The Jackknife test was performed to determine the effect of the assessed variables on the prevalence of the disease and revealed that isothermality has the highest gain when used in isolation. The total frequency of lymphatic filariasis was mapped using the QGIS software to exhibit the suitability of agricultural plantations as breeding grounds for A. poicilius populations. Keywords: Elephantiasis, Maxent, Mosquito, Neglected tropical disease, Vector-borne disease Introduction Lymphatic filariasis is a macroparasitic disease that persists among tropical countries including the Western Pacific Region, the region with the greatest landmass (Chakraborty et al., 2013 ; Khieu et al., 2018 ). The region include several countries, such as the Philippines. It is also home to the parasites responsible for the disease including Wuchereria bancrofti and Brugia malayi and their corresponding vectors (Yajima & Ichimori, 2021 ). As of 2014, over 120 million people have been infected with the disease. Although most are asymptomatic, 40 million people have already been diagnosed with the common symptoms of filariasis, including fever, hydrocoele, lymphedema, and may even develop into elephantiasis (Kron et al., 2000 ; WHO, 2015). In the Philippines, this disease has been determined to be present throughout the archipelago (Kron et al., 2000 ). Despite its prominence in the country, there has yet to be extensive research on its total occurrence, as identification of its endemicity is based on previous health reports and research. As a result, there is a need to estimate the predicted disease occurrence that may have occurred for the past decades with greater efficiency and accuracy by using a robust machine-learning pipeline. Spatial epidemiology involves the spatiotemporal determination of the incidence of a particular disease (Ostfeld et al., 2005 ; Recopuerto-Medina et al., 2024a ; Ricafort et al., 2024 ). One of the methods being employed in predicting the possible risks of a disease is ecological niche modeling (Recopuerto-Medina et al. 2024b ). This mode of modeling is a machine-learning method that allows predicting the probable distribution of a species (Buebos-Esteve et al., 2024 ) or disease (Recopuerto-Medina et al., 2024a ; Limbo-Dizon et al., 2023a ) within a given area depending on the environmental variables or ecological processes provided (Dagamac et al., 2021 ). In most cases, one of the most popular algorithms being used is Maximum Entropy (MaxEnt) due to its adjustability, maneuverability, simplicity, and capability to provide more accurate predictions even with limited data (Moreno-Amat et al., 2015 ; Melo-Merino et al., 2020 ; Buebos-Esteve et al., 2023 ). Due to the various benefits of the model and algorithm used, especially in disease management and control, ecological niche modeling has been increasingly utilized. Numerous studies about the ecological niche modeling of Bancroftian filariasis have already been conducted in other countries, and surveys of areas regarding the endemicity of the disease have already been performed (Slater & Michael, 2012 ; Eneanya et al., 2018 ; Kwarteng et al., 2021 ). However, there have been limited studies regarding the possible distribution of the disease within the entire Philippines. The mapping of lymphatic filariasis and its vector species visually represents the gathered database, which is expected to track the prevalence of mosquito vectors and disease transmissions influenced by specific environmental conditions and factors that contribute to the degree of spread. Kron et al. ( 2000 ) mapped the geographical distribution of lymphatic filariasis in the Philippines and discovered the role of the environment in disease prevalence. Sorsogon and Mindanao are abundant in banana and abaca cultivation, making it an ideal breeding ground for the Bancroftian filariasis vector A. poicilius affecting several individuals tending to the plantation, resulting in higher transmission (Hinz & Hinz, 1985 ). However, the geographical distribution pattern of A. poicilius vectors may also be observed in the outcome, showing the possibility of Bancroftian filariasis transmission in other locations not commonly explored through 1985 to 2019. The plausible environmental factors affecting the distribution include climate changes in temperature and rainfall patterns and wind or changing ecosystems (Parham et al., 2015 ; Recopuerto-Medina et al., 2024b ). According to WHO, in 2013, the majority of lymphatic filariasis cases were caused by W. bancrofti filarial nematodes (Paily et al., 2009 ). Lymphatic filariasis is endemic in multiple provinces in the country (Kron et al., 2000 ). However, the most recent tracking of lymphatic filariasis in the country was determined in 2016, indicating a decrease in endemic provinces affected with filariasis infections (Leonardo et al., 2020 ). Further mapping is important to update the country’s lymphatic filariasis elimination progress. The optimal ecological niche model generated is expected to have the most data information with a 1.0 AUC value that indicates the model’s high accuracy (Almadrones-Reyes & Dagamac, 2018 ). Furthermore, data regarding the environment-based distribution pattern of the associated filarial vector, Aedes poicilius , that may influence its population size have been insufficient (Leonardo et al., 2020 ). Therefore, additional information regarding this matter must be further explored. Species distribution modeling of the vector through Geographic Information Systems (GIS) and machine learning algorithms are modern tools that can be applied to disease epidemiology (Comia-Geneta et al., 2024 ; Recopuerto-Medina et al., 2024b ). MaxEnt modeling generates maps that serve as significant communication tools, allowing identification of the prevalence of lymphatic filariasis in the Philippines, a graphical representation of disease transmission trends, and the prediction of the distribution range of the species involved in transmission (Slater & Michael, 2012 ). The general goal of this study is to create a predictive model via MaxEnt that would determine the possible occurrence of A. poicilius in consideration of selected environmental variables that favor the mosquito species. Materials and methods Study design The geospatial analysis of the distribution of Bancroftian filariasis was conducted from August 2021 to December 2021. This research study focused on one of the principal vectors of Bancroftian filariasis in the Philippines, A. poicilius , in each of the three major island groups—Luzon, Visayas, and Mindanao—including all 81 provinces (Fig. 1 ). The distribution and density of this particular filarial vector heavily relies on extensive plantings of abaca (Musa textilis) , banana (Musa sapientum) , taro (Colocasia) , and screwpine (Pandanus) as A. poicilius larvae have been reported to dwell in the water-filled leaf axils of these plants. In the Philippines, agriculture is a dominant form of livelihood (Kliem, 2024 ), and many provinces, including Marinduque (MIMAROPA), Bicol (Region V), and Sarangani (SOCCSKSARGEN) perhaps are favorable breeding sites of A. poicilius and, thus, remain endemic for Bancroftian filariasis in the country. Consequently, the exposure of individuals tending to these plantations could be considerably higher at certain places and times of the year. Fig. 1. Open in a new tab A map of the Philippines Two main steps were followed to accomplish the study following the procedures of Recopuerto-Medina et al., ( 2024b ): data collection and data processing through MaxEnt modeling and QGIS (Fig. 2 ). QGIS, an easily accessible mapping application, was utilized to visualize and analyze known datasets by incorporating this information into a map layout (Limbo-Dizon & Dagamac, 2023b ). A visual representation of the collated database occurrence of A. poicilius was mapped by generating distribution maps. Furthermore, maximum entropy is a presence-background modeling technique (Tadios-Ayson et al., 2024 ) applied in this study to provide a stable prediction of whether there is greater transmission of filariasis in areas with environmental conditions favorable to A. poicilius . The MaxEnt software is useful even with an incomplete database of the vector species, which is advantageous to this study due to limited A. poicilius and filariasis studies conducted in previous years. The construction of an updated predictive model on the extent of reported Bancroftian filariasis cases is designed to allow the complete eradication of Bancroftian filariasis in the Philippines. Fig. 2. Open in a new tab Methodology Flowchart Protocols and equipment Collection of lymphatic filariasis (LF) occurrence data In accordance to the publication of Recopuert-Medina et al., ( 2024b ), the inclusion of data to be used for analysis was based on the following criteria: (Almadrones-Reyes and Dagamac 2018 ) electronic references such as online journal articles and online databases that would be retrieved from both Google Scholar and PubMed search engines (Aure et al. 2016 ), time period of the references shall strictly fall within January 1985 until December 2019, and (Badia-Rius et al. 2019 ) references that may represent the distribution of lymphatic filariasis in all regions of the Philippines caused by W. bancrofti . The four (Bellone and Failloux 2020 ) major keywords used in the specified search engines were “ Wuchereria bancrofti ,” “ Aedes poecilus ,” “ Aedes poicilius ,” “Bancroftian filariasis,” “lymphatic filariasis,” and “filariasis”. The primary search queries were the mentioned keywords combined with the word “Philippines.” After compiling all chosen references from the searches, these items were further screened to see if any duplicate articles would be included and would be eligible for inclusion. Articles identified as not fitting within the indicated criteria were discarded. Georeferencing of literatures and databases The studies and databases collected were geo-referenced whether the coordinates will be provided or only the name of the location will be given. For areas with coordinates, verification of the existence of the area was ensured with the use of Google Earth. Double-checking of the areas with coordinates was also done through Google Maps. For areas with unknown coordinates, the location was checked using Google Maps, Google Earth, and GeoNames (Badia-Rius et al., 2019 ). Non-confirmation or imprecision of the location provided in an article was the basis for its removal from data analysis. The acquired coordinates were then converted from the degrees minutes seconds (DMS) format into decimal degrees for MaxEnt (Dagamac et al., 2021 ). The geo-referenced data were imported into QGIS 3.16.6 for map creation (Tadios-Ayson et al., 2024 ). Selection of climatic and environmental variables In order to produce a model that may detect the presence of A. poicilius that may harbor lymphatic filariasis, a total of twenty (Kwarteng et al. 2021 ) environmental variables related to the topography and climate of the regions were initially extracted from the WorldClim database (Recopuerto-Medina et al., 2024a ) as these factors influence the capacity of the species to survive and proliferate. These factors, based on Badia-Rius et al. ( 2019 ) and Moffett ( 2007 ), should include the following listed as Table 1 . Analysis was done using the ENMTools package in the R software (Limbo-Dizon et al., 2022), and a value of ± 0.75 must be achieved to be utilized for ecological niche modeling (Mwakapeje et al., 2019 ). Table 2. Bioclimatic variables and their percent contributions to model performance Variable Variable Description Percent Contribution Bio3 Isothermality 37.2 Bio7 Temperature annual range 15.6 Bio2 Mean diurnal range 13.2 Bio12 Annual precipitation 11.4 Open in a new tab Table 1. Bioclimatic variables and their corresponding unit Code Variable Unit BIO1 Annual Mean Temperature °C BIO2 Mean Diurnal Range (Mean of monthly (max temp - min temp)) °C BIO3 Isothermality (BIO2/BIO7) (×100) % BIO4 Temperature Seasonality (standard deviation ×100) °C BIO5 Max Temperature of Warmest Month °C BIO6 Min Temperature of Coldest Month °C BIO7 Temperature Annual Range (BIO5-BIO6) °C BIO8 Mean Temperature of Wettest Quarter °C BIO9 Mean Temperature of Driest Quarter °C BIO10 Mean Temperature of Warmest Quarter °C BIO11 Mean Temperature of Coldest Quarter °C BIO12 Annual Precipitation Mm BIO13 Precipitation of Wettest Month Mm BIO14 Precipitation of Driest Month Mm BIO15 Precipitation Seasonality (Coefficient of Variation) % BIO16 Precipitation of Wettest Quarter Mm BIO17 Precipitation of Driest Quarter Mm BIO18 Precipitation of Warmest Quarter Mm BIO19 Precipitation of Coldest Quarter Mm Open in a new tab Modeling of lymphatic filariasis distribution Model creation was executed using the MaxEnt software version 3.4.4 (Buebos-Esteve et al., 2023 ), wherein grid squares that may contain the same documented occurrence of the vector or disease were removed. Fitting of the model was done with the following settings: a total of 100 bootstrap replicates, an allocation of 70% of the dataset towards training data, and a designation of 30% of the dataset towards data to be used for testing model accuracy (Dagamac et al., 2021 ). A total of 10,000 background points were also created. Other parameters included were auto features, convergence threshold = 0.00001, and a regularization multiplier with a value of 1 (Dagamac et al., 2021 . However, model parameters that optimize the model’s accuracy were adjusted using the ENMeval package (Muscarella et al., 2014 ). Data visualization and analyses Model evaluation Continuous maps of the vector A. poicilius were obtained through logistic output. This expresses the suitability of A. poicilius in terms of probability values, which ranged from 0 (unsuitable/nonsuitable) to 1 (highest suitability). The area under the curve (AUC) of the receiver operating characteristic (ROC) statistics was used for model accuracy evaluation. The AUC is an important threshold-independent index used in the evaluation of the predictive performance of a certain model by discriminating presence data from background data, which will serve as pseudo-absences (Machado-Machado, 2012 ). Values for AUC range from 0 to 1; values less than or equal to 0.5 (AUC ≤ 0.5) indicate that the generated model’s predictivity is equal to or worse than a random prediction (Almadrones-Reyes & Dagamac, 2018 ). In contrast, values greater than 0.75 correspond to a model with greater robustness of niche prediction (Phillips et al., 2006 ). Specifically, there are five (Buebos-Esteve et al. 2023 ) subcategories: 0.5–0.6 fail or insufficient performance; 0.6–0.7 poor performance; 0.7–0.8 fair or average performance; 0.8–0.9 good performance; and 0.9–1.0 excellent performance (Swets, 1988 ; Phillips et al., 2006 ; Almadrones-Reyes & Dagamac, 2018 ). Assessment of environmental variables To assess the importance of the environmental variables used in modeling, the MaxEnt model utilizes the Jackknife test. The Jackknife cross-evaluation procedure quantitatively describes the effect of an environmental factor when used in isolation and when omitted from the model (Shcheglovitova & Anderson, 2013 ). This method involves three (Badia-Rius et al. 2019 ) specific processes: (Almadrones-Reyes and Dagamac 2018 ) calculation of the training gain using all variables in the model; (Aure et al. 2016 ) calculation of the training gain for the model using each variable in isolation; and (Badia-Rius et al. 2019 ) calculation of the training gain using all the variables with one variable excluded (Wang et al., 2018 ; Sun et al., 2020 ). Visual interpretation of distribution maps The data obtained from MaxEnt were imported to the QGIS software v.3.16.6 and ArcMap software to create and visualize the final map (Tadios-Ayson et al., 2024 ) with the highest potential for the vector to spread. This displays the map of the study area, the Philippines, which was subdivided into the following categories depending on the occurrence probability of the vector A. poicilius : ≤ 0.20; 0.20–0.40; 0.40–0.60; 0.60–0.80; and ≥ 80. The prediction model was classified using the appropriate color theme according to the prediction values. Regions with the lowest prediction value were designated with green, while the highest prediction value had a red color. Results A total species occurrence point of 2,003 was collected for generating ecological niche models. Of these occurrence points, 70% were used for training data and 30% for test data. The model generated through MaxEnt shows the mean omission rate and predicted area as a function of the cumulative threshold for A. poicilius (Fig. 3 ). The omission rate is the fraction of test localities not predicted as suitable for the species, and the predicted area is the fraction that is predicted to be ideal for A. poicilius . Fig. 3. Open in a new tab Average omission and predicted area for A. poicilius Figure 4 shows the receiver operating characteristic (ROC) curve for the same generated data. The specificity is determined using predicted area and not from true commission, so the maximum AUC that can be obtained is less than 1. The ecological niche modeling in MaxEnt yielded an average AUC value of 0.995 with a standard deviation of 0.001, indicating the predictive model’s excellent performance. Out of the twenty (Kwarteng et al. 2021 ) environmental variables used in creating the model, four (Bellone and Failloux 2020 ) main bioclimatic variables contained the most useful information in explaining the distribution of A. poicilius . Specifically, isothermality (Bio3), temperature annual range (Bio7), mean diurnal range (Bio2), and annual precipitation (Bio12) have the greatest percent contributions to the model, as shown in Table 2 . Fig. 4. Open in a new tab Average sensitivity vs. 1-specificity for A. poicilius The response curves of A. poicilius to the four assessed bioclimatic variables are shown in Fig. 5 . Among the four, Bio3 (isothermality) has the greatest effect on the model, with a 37.2% contribution. The survival probability of A. poicilius is the highest at 0.9 when the isothermality (Bio3) has a value of around 74. On the other hand, the suitability of A. poicilius is highest at 0.8 when the temperature annual range (Bio7) is around 93. For Bio2, the response curve shows that the suitability is highest at 0.9 when the mean diurnal range is at 78 but decreases beyond this value. Lastly, results indicate that the predicted environmental suitability of A. poicilius is optimal at an annual precipitation (Bio12) of 2750 mm. Fig. 5. Open in a new tab Response curves of the four main bioclimatic variables. a Isothermality (Bio3), b Temperature Annual Range (Bio7), c Mean Diurnal Range (Bio2), d Annual Precipitation (Bio12) Based on the occurrence records of A. poicilius in the Philippines, the vector species are primarily found in Bicol (Region 5) and Western Visayas (Region 6). The distribution model generated through QGIS predicted several municipalities across the country to be highly suitable for A. poicilius (Fig. 6 a). Areas with the highest predicted environmental suitability for A. poicilius included Sorsogon (Anog), Viga, and Buenavista (Fig. 6 a). Fig. 6. Open in a new tab a Distribution model of A. poicilius georeferenced occurrence points (red points) using QGIS and b rasterized map generated from ArcMap The ArcMap model for A. poicilius uses a color scale from green to red, indicating the habitat suitability value from 0 to 1 (Fig. 6 b). Orange has a range value of 0.60–0.80, which indicates a high probability of suitable conditions, while red values of > 0.80 indicate the highest suitability for A. poicilius , light green (0.20–0.40) to yellow (0.60–0.80) indicates moderate conditions where A. poicilius is found, and green indicates the lowest probability of suitable conditions with values < = 0.20. For A. poicilius , suitable conditions are predicted to be highly probable through most of Bicol (Region V), Western Visayas (Region VI), and the upper part of Eastern Visayas (Region VIII). Discussion The study used an ecological niche modeling approach to predict the potential geographic distribution of A. poicilius across the Philippine archipelago. This effort is the first distribution model to incorporate georeferenced occurrence data emphasizing environmental variables to predict the potential distribution of this parasite and vector species. The distribution models of A. poicilius coincide with the study of Kron et al. ( 2000 ) and Aure et al. ( 2016 ), wherein municipalities within the Bicol region (Region V) and Panay Island (Region VI), respectively, are endemic areas for bancroftian filariasis. In Panay Island, these areas are categorized under Type I climate based on Coronas’ Climate Classification, wherein rain falls throughout the year, with particularly heavy rains from June to November (Gatpatan et al., 2024 ). Meanwhile, the dry season from December to May rendered the species almost undetectable (Gatpatan et al., 2024 ). Other areas within the region also fall under the Type III climate classification, wherein the dry season lasts from November to April while the wet season occurs for the other months (Dagamac et al., 2017 ). The Bicol region is characterized by three climate types: Type II, wherein only a wet season occurs with the heaviest rains occurring from November to January; Type III, and Type IV, wherein there is a balanced rainfall distribution for the entire year. One of the primary agricultural products of these regions are the abaca plant (Musa textilis) and banana plant ( Musa spp. ) . A. poicilius relies heavily on abaca and banana plants as the larvae utilize the water-filled leaf axils as breeding sites (Aure et al., 2016 ). Hence, it provides highly suitable conditions for the vector species to thrive, resulting in the highest abundance of the species. High suitability of A. poicilius in Inland Panay, as shown in the distribution map, can be backed up by the presence of abaca such as those found in Barangay Hilwan and Barangay Maliao in Tapaz, Capiz (Department of Labor and Employment, 2013) or are even validated through the mosquito surveys that were performed in Panay Island (Aure et al., 2016 ) that are in close proximity to the determined red-zone areas. The main bioclimatic predictors associated with A. poicilius distribution, Bio3 (Isothermality), Bio7 (Temperature Annual Range), Bio2 (Mean Diurnal Range), and Bio12 (Annual Precipitation), correlate with fluctuations in temperature and precipitation throughout the year. As mosquitoes are ectotherms, these animals are dependent on acquiring heat from the environment. Thus, locations where these mosquitoes may inhabit are limited by temperature (Buebos-Esteve & Dagamac, 2024 ). Various physiological activities of mosquitoes rely on optimal temperature to continue their life cycle, including egg hatching, larvae and pupae development, and adult survivability (Bellone & Failloux, 2020 ). Gonotrophic cycles of mosquitoes, such as that in Aedes aegypti , are also observed to be influenced by temperature. A negative relationship is exhibited as the rise in temperature shortens the period of extracting blood meal from the host until the eggs are released. Due to this occurrence, it is likely that A. poicilius also has similar outcomes in terms of gonotrophic cycle length (Carrington et al., 2013 ). Moreover, the population density of mosquitoes relies heavily on temperature. A study by Ezeakacha and Yee ( 2019 ) determined that a larger quantity of mosquitoes could remain alive when these individuals were exposed to raised temperatures during their larval stage compared to cooler temperatures. Adult female mosquitoes must have developed in lower temperatures during the larval stage for fecundity to produce more offspring (Sibly & Atkinson, 1994Kersten et al.,2019). Conclusion This study serves as the initial attempt to collate multiple occurrence points of A. poicilius throughout the entire country compared to regional-based research. From 1985 to 2019, over 2,003 A. poicilius occurrence points that potentially harbor Bancroftian filariasis have been collected and utilized in generating a distribution map through QGIS. The sensitivity of the MaxEnt model was authenticated using the area under the curve (AUC) method. The model had a mean training AUC of 0.995 ± 0.001, indicating that the model created is excellent. One major limitation of the study involves data accessibility, as most studies collected were published during the late 20th century. As mentioned by Salonga et al. ( 2021 ), updates need to be made from the Department of Health (DOH) and other local research institutions on the status of disease transmission rates of lymphatic filariasis and the different vectors. By collecting more data, the evaluation of the generated ecological niche model may be further enhanced and will be up-to-date. Another limitation would be excluding other environmental variables used in modeling, such as altitude. Despite these limitations, the model generated has been able to determine bioclimatic variables such as isothermality, mean diurnal range, and annual precipitation to influence the possible occurrence of the mosquito vector. Such information now provides environmental thresholds that make it suitable for a vector to thrive and spread the disease per se. Moreover, the current research focuses on determining the potential distribution of lymphatic filariasis through its known record of occurrence of its vector, A. poicilius . Such predictive maps may generate new potentialities when more occurrence points of the vector are collected. As the possible disease continues to occur in the next years or decade, the maps and models this study showed can be of great use in terms of public health information and possible mitigation of the vector by knowing the possible “hotspots” for disease. There are many other modeling that can be done to further understand the mechanisms of the vector. Like performing an ecological niche modeling of other vector mosquitoes, such as other Aedes and Anopheles species in the Philippines or the parasite W. bancrofti itself, because this can provide a better interaction in terms of thecurrent situation in the ground how this neglected vector-borne disease affects the areas of the Philippines. Also, other environmental parameters, such as the elevation or presence of waterways or anthropogenic parameters, could also be considered when modeling the putative mosquito distribution. Nevertheless, this first species distribution map for lymphatic filariasis in the Philippines contributes significantly to understanding the potential abiotic and epidemiological requirement for such neglected tropical diseases whose infection has affected the lives of many marginalized Filipinos. Acknowledgements NHAD acknowledges the support of DOST-PCAARRD as a Balik Scientist Grantee and the UST-RCNAS for the small financial support needed to conduct this collaborative research study. Data availability The corresponding author makes the data used in this research available upon reasonable request. Declarations Competing interests The authors declare no competing interests. Ethics approval Not Applicable. Consent to participate Not applicable. Consent for publication All of the authors consented to publish this manuscript. The authors confirm that this article is original research and has not been published or presented previously in any journal or conference in any language in whole or in part. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. References Almadrones-Reyes KJ, Dagamac NHA (2018) Predicting local habitat suitability in changing climate scenarios: applying species distribution modeling for Diderma hemisphaericum. Curr Res Environ App Mycol 8(5):492–500. 10.5943/cream/8/5/2 [ Google Scholar ] Aure W, Torno M, Malijan RP, Cruz E, Hernandez L, Baquilod M, Bangs M, Salazar FV (2016) Investigation of mosquitoes with emphasis on Aedes (Finlaya) Poicilius , putative vector of bancroftian filariasis on Panay Island, the Philippines. 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