These data represent mosquito trap site results in the District of Columbia from 2016 to 2018. Trap locations are considered approximate address and/or the “nearest” street address or block to the stated coordinates in the data. Visit Fight the Bite: Protecting the District of Columbia from Mosquitoes- a collection of the 2016-2018 Arbovirus Surveillance Program conducted annually by DC Health, Health Regulation & Licensing Admin., Animal Services Div.Mosquitoes have the potential to spread harmful diseases. During the annual mosquito season in Washington DC, usually from April – October, DC Health deploys surveillance and mitigation methods to control the mosquito population in the District. DC Health (also known as the D.C. Department of Health or formerly DOH) has been trapping and testing mosquitoes for West Nile virus (WNV) for well over a decade. Starting in 2016, and in response to the Zika outbreak in Latin America and the Caribbean, DC Health substantially increased mosquito monitoring activities across the city. There were a total of 28 sites and 36 traps across the 8 wards. Data was submitted to the Centers for Disease Control MoquitoNet Portal.Note: the 2017 analysis does not include data for October. This is because October of 2017 would have skewed the results far too much based on a few variables that occurred. For example, the number of traps which had failed by the end of the season.Mosquito species in Washington, D.C.:Culex Pipiens, Salinarius and Culex Restuan: spread West Nile VirusAedes aegypti : according to the Centers for Disease Control (CDC), health experts have determined this species to be the most competent vector, capable of transmitting Zika to the human population. To date, none of the Aedes aegypti trapped in Washington, D.C. have been found to carry the Zika virus.Aedes albopictus: capable of spreading Zika to people. However, health experts are still learning whether it is likely to do so as it appears at this time, it is not as competent a vector for transmitting Zika as is the Aedes aegypti. Just because a mosquito can carry the virus does not mean that it will cause disease. So far, none of the Aedes albopictus trapped in Washington, D.C. have been found to carry the Zika virus.Aedes japonicus: normally found in South Florida, is present in D.C. in small numbers. Presently there is no indication that they are competent vectors for spreading Zika to the human population.

Capture results of mosquitoes from various locations in Edmonton. These collections are from standard New Jersey light traps that are commonly used to record changes in abundance of mosquitoes before and after control campaigns and to compare seasonal and annual fluctuations in population. Since not all mosquito species are attracted equally to light traps, the City uses a variety of other trapping and survey methods (with their own limitations) to monitor mosquitoes. Not all trap collection sites are factored into the historical averages. Some data can be incomplete due to trap failure. Some trap locations change over time. Trap collections reflect, not absolute population levels, but mosquito activity, which is influenced by changing environmental conditions (temperature, humidity, wind, etc.). The weekly averages do not include any male mosquitoes or any females of species that do not typically bite people. Each data set reflects the mosquito activity of the week previous to the collection date.

To complement this dataset, there is the Rainfall Gauge data which measures rainfall data in the Greater Edmonton area:

https://data.edmonton.ca/Environmental-Services/Rainfall-Gauge-Results/7fus-qa4r

Capture results of mosquitoes from various locations in Edmonton. These collections are from CO2 traps that are used to record changes in abundance of mosquitoes before and after control campaigns and to compare seasonal and annual fluctuations in population. Not all trap collection sites are factored into the historical averages. Some data can be incomplete due to trap failure. Some trap locations change over time. Trap collections reflect, not absolute population levels, but mosquito activity, which is influenced by changing environmental conditions (temperature, humidity, wind, etc.). The weekly averages do not include any male mosquitoes or any females of species that do not typically bite people. Each data set reflects the mosquito activity of the week previous to the collection date.

To complement this dataset, there is the Rainfall Gauge data which measures rainfall data in the Greater Edmonton area:

https://data.edmonton.ca/Environmental-Services/Rainfall-Gauge-Results/7fus-qa4r

This dataset contains positive cases of West Nile virus found in humans by county of residence, 2006-present. Humans usually become infected with West Nile virus by being bitten by an infected mosquito. Viruses carried in the mosquito’s saliva enter the blood stream and local tissues where they infect immune cells. Most of the people who do become sick during a WNV infection develop what is referred to as “West Nile fever.” A small percentage of people will develop a much more serious illness called West Nile neuroinvasive disease (WNND). Positive cases in this dataset include both West Nile fever and West Nile neuroinvasive disease.

BackgroundOne major consequence of economic development in South-East Asia has been a rapid expansion of rubber plantations, in which outbreaks of dengue and malaria have occurred. Here we explored the difference in risk of exposure to potential dengue, Japanese encephalitis (JE), and malaria vectors between rubber workers and those engaged in traditional forest activities in northern Laos PDR.Methodology/Principal findingsAdult mosquitoes were collected for nine months in secondary forests, mature and immature rubber plantations, and villages. Human behavior data were collected using rapid participatory rural appraisals and surveys. Exposure risk was assessed by combining vector and human behavior and calculating the basic reproduction number (R0) in different typologies. Compared to those that stayed in the village, the risk of dengue vector exposure was higher for those that visited the secondary forests during the day (odds ratio (OR) 36.0), for those living and working in rubber plantations (OR 16.2) and for those that tapped rubber (OR 3.2). Exposure to JE vectors was also higher in the forest (OR 1.4) and, similar when working (OR 1.0) and living in the plantations (OR 0.8). Exposure to malaria vectors was greater in the forest (OR 1.3), similar when working in the plantations (OR 0.9) and lower when living in the plantations (OR 0.6). R0 for dengue was >2.8 for all habitats surveyed, except villages where R0≤0.06. The main malaria vector in all habitats was Anopheles maculatus s.l. in the rainy season and An. minimus s.l. in the dry season.Conclusions/SignificanceThe highest risk of exposure to vector mosquitoes occurred when people visit natural forests. However, since rubber workers spend long periods in the rubber plantations, their risk of exposure is increased greatly compared to those who temporarily enter natural forests or remain in the village. This study highlights the necessity of broadening mosquito control to include rubber plantations.

The WHO have identified 20 neglected tropical diseases (NTDs), both communicable and non-communicable, that prevail in tropical and subtropical conditions in 149 countries. The NTD portfolio …Show full descriptionThe WHO have identified 20 neglected tropical diseases (NTDs), both communicable and non-communicable, that prevail in tropical and subtropical conditions in 149 countries. The NTD portfolio currently includes: • Buruli ulcer • Chagas disease • Dengue and Chikungunya • Dracunculiasis (guinea-worm disease) • Echinococcosis • Foodborne trematodiases • Human African trypanosomiasis (sleeping sickness) • Leishmaniasis • Leprosy (Hansen's disease) • Lymphatic filariasis • Mycetoma, chromoblastomycosis and other deep mycoses • Onchocerciasis (river blindness) • Rabies • Scabies and other ectoparasites • Schistosomiasis • Soil-transmitted helminthiases • Snakebite envenoming • Taeniasis/Cysticercosis • Trachoma • Yaws (Endemic treponematoses) Of the currently noted NTDs, only chikungunya, dengue, leprosy (Hansen’s disease) and rabies are nationally notifiable in Australia. Chikungunya Chikungunya is not currently endemic in Australia. There have been no reported cases of locally-acquired chikungunya in Australia, though mosquitoes capable of spreading the virus are present in some areas of Queensland. From 2015 to 2020, the number of notified chikungunya cases in Australia has ranged between 32 and 114 annually, with a mean of 81 cases (Table 1). Between 2015 and 2020, notified Chikungunya infections in Australia were most frequently acquired in areas of South and South East Asia, particularly India and Indonesia, and the Pacific Islands. Trends in overseas acquisition are influenced by the volume and frequency of travel to source countries and their local chikungunya epidemiology. Dengue Dengue is not currently endemic in Australia, but outbreaks associated with locally acquired cases do occur in coastal areas of mainland North Queensland, where the Aedes aegypti mosquito is present in suitable environments near susceptible populations. The number of notified dengue cases in Australia from 2015 to 2020 have ranged between 222 and 2,238 annually, with a mean of 1,284 cases (Table 1). In Australia, overseas-acquired dengue infections are most frequently acquired in South East Asia, particularly Indonesia. Trends in overseas acquisition are influenced by the volume and frequency of travel to source countries and their local dengue epidemiology. On average, over 90% of dengue cases reported annually in Australia are overseas acquired. Leprosy Leprosy is an uncommon disease in Australia with the majority of cases being diagnosed in migrants from leprosy endemic countries and occasionally in local Aboriginal and Torres Strait Islander populations. In 2020, a total of 6 cases of leprosy were notified (Table 1), representing a rate of less than 0.1 case per 100,000 population. Between 2015 and 2020, annual notifications of leprosy in Australia have ranged from 6 to 21 cases per year (Table 1). Rabies Australia is considered to be free of rabies with the last overseas acquired case being reported in 1987. *The data provided were extracted from the National Notifiable Disease Surveillance System (NNDSS) on 23 February 2021. Due to the dynamic nature of the NNDSS, data in this extract are subject to retrospective revision and may vary from data reported in published NNDSS reports and reports of notification data by states and territories. Trachoma Australia is a signatory to the World Health Organization (WHO) Alliance for the Global Elimination of Trachoma by 2020. Elimination of trachoma as a public health problem is defined by the WHO as ‘community prevalence of trachoma in children aged 1-9 years of less than 5%’. As part of its WHO obligation to eliminate trachoma, Australia is required to regularly collect data on trachoma prevalence. The National Trachoma Surveillance and Reporting Unit, managed by the Kirby Institute, University of NSW, provides surveillance and annual reporting of trachoma prevalence, using State and Territory Governments’ data. Trachoma program activities, data collection and analysis are guided by the National Guidelines for the Public Health Management of Trachoma in Australia (revised in 2013 and published in 2014 – see link). The below information should be read in conjunction with the Guidelines. In 2019, 115 communities were identified as being ‘at-risk’ of trachoma. A total of 4419 people received antibiotic (azithromycin) treatment for trachoma (including people diagnosed with trachoma, their household contacts and community members as required by the Guidelines). This is fewer doses of azithromycin delivered in 2019 as compared to 2018 (4419 compared to 6576). Strong progress has been made in reducing the overall prevalence of active trachoma rate from 14% in 2009 to 4.5% in 2019. Further information can be found at: http://www.health.gov.au/internet/main/publishing.nsf/Content/health-oatsih-pubs-trachreport ; and http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-cdna-pubs-trachoma.htm

DNA from individual Aedes aegypti mosquitoes was extracted and used for genotyping at 50,000 loci distributed along the species genome, using the Axiom Aegypti1 SNP chip (Life Technologies Corporation CAT#550481). Files "all_snps_G3Dryad" and "Replicas_SNPchip" contain all 50,000 SNPs genotyped, prior to filtering. File "50k_SNPs_30_samples_LD_MAF_miss_FINAL" contain the SNPs after applying filters in Plink 1.9 (https://www.cog-genomics.org/plink/) for linkage disequilibrium (LD: -indep-pairwise 50 10 0.3), minor allele frequency (MAF: -maf 0.1) and missing data (-geno 0.1).

Forecasting the impacts of climate change on Aedes-borne viruses—especially dengue, chikungunya, and Zika—is a key component of public health preparedness. We apply an empirically parameterized model of viral transmission by the vectors Aedes aegypti and Ae. albopictus, as a function of temperature, to predict cumulative monthly global transmission risk in current climates, and compare them with projected risk in 2050 and 2080 based on general circulation models (GCMs). Our results show that if mosquito range shifts track optimal temperature ranges for transmission (21.3–34.0°C for Ae. aegypti; 19.9–29.4°C for Ae. albopictus), we can expect poleward shifts in Aedes-borne virus distributions. However, the differing thermal niches of the two vectors produce different patterns of shifts under climate change. More severe climate change scenarios produce larger population exposures to transmission by Ae. aegypti, but not by Ae. albopictus in the most extreme cases. Climate-driven risk of transmission from both mosquitoes will increase substantially, even in the short term, for most of Europe. In contrast, significant reductions in climate suitability are expected for Ae. albopictus, most noticeably in southeast Asia and west Africa. Within the next century, nearly a billion people are threatened with new exposure to virus transmission by both Aedes spp. in the worst-case scenario. As major net losses in year-round transmission risk are predicted for Ae. albopictus, we project a global shift towards more seasonal risk across regions. Many other complicating factors (like mosquito range limits and viral evolution) exist, but overall our results indicate that while climate change will lead to increased net and new exposures to Aedes-borne viruses, the most extreme increases in Ae. albopictus transmission are predicted to occur at intermediate climate change scenarios.

Arboviral disease transmission by Aedes mosquitoes poses a major challenge to public health systems in Ecuador, where constraints on health services and resource allocation call for spatially informed management decisions. Employing a unique dataset of larval occurrence records provided by the Ecuadorian Ministry of Health, we used ecological niche models (ENMs) to estimate the current geographic distribution of Aedes aegypti in Ecuador, using mosquito presence as a proxy for risk of disease transmission. ENMs built with the Genetic Algorithm for Rule-Set Production (GARP) algorithm and a suite of environmental variables were assessed for agreement and accuracy. The top model of larval mosquito presence was projected to the year 2050 under various combinations of greenhouse gas emissions scenarios and models of climate change. Under current climatic conditions, larval mosquitoes were not predicted in areas of high elevation in Ecuador, such as the Andes mountain range, as well as the eastern portion of the Amazon basin. However, all models projected to scenarios of future climate change demonstrated potential shifts in mosquito distribution, wherein range contractions were seen throughout most of eastern Ecuador, and areas of transitional elevation became suitable for mosquito presence. Encroachment of Ae. aegypti into mountainous terrain was estimated to affect up to 4,215 km2 under the most extreme scenario of climate change, an area which would put over 12,000 people currently living in transitional areas at risk. This distributional shift into communities at higher elevations indicates an area of concern for public health agencies, as targeted interventions may be needed to protect vulnerable populations with limited prior exposure to mosquito-borne diseases. Ultimately, the results of this study serve as a tool for informing public health policy and mosquito abatement strategies in Ecuador.

As the world’s fastest spreading vector-borne disease, dengue was estimated to infect more than 390 million people in 2010, a 30-fold increase in the past half century. Although considered to be a non-endemic country, mainland China had 55,114 reported dengue cases from 2005 to 2014, of which 47,056 occurred in 2014. Furthermore, 94% of the indigenous cases in this time period were reported in Guangdong Province, 83% of which were in Guangzhou City. In order to determine the possible determinants of the unprecedented outbreak in 2014, a population-based deterministic model was developed to describe dengue transmission dynamics in Guangzhou. Regional sensitivity analysis (RSA) was adopted to calibrate the model and entomological surveillance data was used to validate the mosquito submodel. Different scenarios were created to investigate the roles of the timing of an imported case, climate, vertical transmission from mosquitoes to their offspring, and intervention. The results suggested that an early imported case was the most important factor in determining the 2014 outbreak characteristics. Precipitation and temperature can also change the transmission dynamics. Extraordinary high precipitation in May and August, 2014 appears to have increased vector abundance. Considering the relatively small number of cases in 2013, the effect of vertical transmission was less important. The earlier and more frequent intervention in 2014 also appeared to be effective. If the intervention in 2014 was the same as that in 2013, the outbreak size may have been over an order of magnitude higher than the observed number of new cases in 2014.The early date of the first imported and locally transmitted case was largely responsible for the outbreak in 2014, but it was influenced by intervention, climate and vertical transmission. Early detection and response to imported cases in the spring and early summer is crucial to avoid large outbreaks in the future.

Abstract

The 2021 Nigeria Malaria Indicator Survey (NMIS) was implemented by the National Malaria Elimination Programme (NMEP) of the Federal Ministry of Health (FMoH) in collaboration with the National Population Commission (NPC) and National Bureau of Statistics (NBS).

The primary objective of the 2021 NMIS was to provide up-to-date estimates of basic demographic and health indicators related to malaria. Specifically, the NMIS collected information on vector control interventions (such as mosquito nets), intermittent preventive treatment of malaria in pregnant women, exposure to messages on malaria, care-seeking behaviour, treatment of fever in children, and social and behaviour change communication (SBCC). Children age 6–59 months were also tested for anaemia and malaria infection. The information collected through the NMIS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Woman age 15-49

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2021 NMIS was designed to provide most of the survey indicators for the country as a whole, for urban and rural areas separately, and for each of the country’s six geopolitical zones, which include 36 states and the Federal Capital Territory (FCT). Nigeria’s geopolitical zones are as follows: • North Central: Benue, Kogi, Kwara, Nasarawa, Niger, Plateau, and FCT • North East: Adamawa, Bauchi, Borno, Gombe, Taraba, and Yobe • North West: Jigawa, Kaduna, Kano, Katsina, Kebbi, Sokoto, and Zamfara • South East: Abia, Anambra, Ebonyi, Enugu, and Imo • South South: Akwa Ibom, Bayelsa, Cross River, Delta, Edo, and Rivers • South West: Ekiti, Lagos, Ogun, Osun, Ondo, and Oyo

The 2021 NMIS used the sample frame for the proposed 2023 Population and Housing Census (PHC) of the Federal Republic of Nigeria. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), each LGA is divided into wards, and each ward is divided into localities. Localities are further subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster unit for the 2021 NMIS, was defined on the basis of EAs for the proposed 2023 PHC.

A two-stage sampling strategy was adopted for the 2021 NMIS. In the first stage, 568 EAs were selected with probability proportional to the EA size. The EA size is the number of households residing in the EA. The sample selection was done in such a way that it was representative of each state. The result was a total of 568 clusters throughout the country, 195 in urban areas and 373 in rural areas.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2021 NMIS: the Household Questionnaire, the Woman’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Nigeria. After the questionnaires were finalised in English, they were translated into Hausa, Yoruba, and Igbo.

Cleaning operations

The processing of the 2021 NMIS data began immediately after the start of fieldwork. As data collection was being completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. Data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted on any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding open-ended questions. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. Concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables also allowed for effective monitoring. Secondary editing of the data was completed in February 2022. The data processing team coordinated this exercise at the central office.

Response rate

A total of 14,185 households were selected for the survey, of which 13,887 were occupied and 13,727 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 14,647 women age 15-49 were identified for individual interviews. Interviews were completed with 14,476 women, yielding a response rate of 99%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, or incorrect data entry. Although numerous efforts were made during the implementation of the 2021 Nigeria Malaria Indicator Survey (NMIS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2021 NMIS is only one of many samples that could have been selected from the same population, using the same design and expected sample size. Each of these samples would yield results that differ somewhat from the results of the selected sample. Sampling errors are a measure of the variability among all possible samples. Although the exact degree of variability is unknown, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, and so on), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2021 NMIS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed via SAS programmes developed by ICF. These programmes use the Taylor linearisation method to estimate variances for estimated means, proportions, and ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Sampling errors tables are presented in Appendix B of the final report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at ages 14/15
• Age displacement at ages 49/50
• Live births by years preceding the survey
• Completeness of reporting
• Observation of mosquito nets
• Number of enumeration areas completed by month of fieldwork and zone
• Positive rapid diagnostic test (RDT) results by month of fieldwork and zone, Nigeria MIS 2021
• Concordance and discordance between RDT and microscopy results
• Concordance and discordance between national and external quality control laboratories

See details of the data quality tables in Appendix C of the final report.

This data set contains data from the MEGABITESS 2019 cohort including ovitrap locations and characteristics, egg count data, and demographic data.Below are definitions and descriptions for the columns of dataNo - Unique IDSite ID Alpha - yy-site## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteSite ID - yy-site-## where yy is the two digit year, site is a 4 digit school/teacher ID, ## is the number 1-10 for each siteInformal Name - school name, teacher name, site number (1-10)School - school nameEducator - Teacher nameTrap Number - Each school has 10 traps, this is the number of the trap at each schoolElevation - Elevation where the trap was located in feetTotalPopulation - the total number of people in that census tractChildUnder18 - number of children in that census tract that are under 18DateStaratedISO - date in the format yyyymmddDate Started - The day egg traps were set out for the weekDateCollectedISO - date in the format yyyymmddDate Collected - The day the egg traps were collected during the weekEggs_Counted_by - the initials of the person at UTK who counted the eggsDataEntry - Initials of the person at UTK who entered the dataInformal_ID - school name, teacher name, site number (1-10)Calendar Week - The calendar week that the egg traps were set out (1-52)Study Week - Each school set up traps for 10 weeks; this number is a number 1-10Aedes hatched - the number of eggs on the germination paper that had hatchedAedes Embryonating - the number of eggs on the germination paper that had not hatchedOther - the number of eggs of a different speciesTotal - the total number of eggs and embryonating eggsComments - comments about the egg countingAdults_Identified_by - Initials of the person at UTK who coutned the adultsData_Entered_by - Initials of the person at UTK who entered the dataAdult Female mosquitoes - number of adult female mosquitoes that hatchedAdult Male mosquitoes - number of adult male mosquitoes that hatchedIdentification NotesLand Cover -primary land cover where the ovitrap was locatedOther - Land Cover - primary land cover if other was selectedShade Covered - how much shade was at the ovitrap locationNotes Or Comments - notes or comments about the trap locationShade Type - what is causing the shadeOther-Shade Type - cause of shade if otherWater Adjacency - is the ovitrap adjacent to a water sourceLatitude - latitude in decimal degreesLongitude - longitude in decimal degrees-9999 = no data; be sure to filter out the no data values when running any statistics

The increase in hydro dams in the Mekong River amidst the prevalence of multidrug-resistant malaria in Cambodia has raised concerns about global public health. Political conflicts during Covid-19 pandemic led cross-border movements of malaria cases from Myanmar and caused health care burden in Thailand. While previous publications used climatic indicators for predicting mosquito-borne diseases, this research used globally recognizable World Bank indicators to find the most impactful indicators related with malaria and shed light on the predictability of mosquito-borne diseases. The World Bank datasets of the World Development Indicators and Climate Change Knowledge Portal contain 1494 time series indicators. They were stepwise screened by Pearson and Distance correlation. The sets of five and four contain respectively 19 and 149 indicators highly correlated with malaria incidence which were found similarly among five and four GMS countries. Living areas, ages, career, income, technology accessibility, infrastructural facilities, unclean fuel use, tobacco smoking, and health care deficiency have affected malaria incidence. Tonle Sap Lake, the largest freshwater lake in Southeast Asia, could contribute to the larval habitat. Seven groups of indicator topics containing 92 indicators with not-null datapoints were analyzed by regression models, including Multiple Linear, Ridge, Lasso, and Elastic Net models to choose 7 crucial features for malaria prediction via Long Short Time Memory network. The indicator of people using at least basic sanitation services and people practicing open defecation were health factors had most impacts on regression models. Malaria incidence could be predicted by one indicator to reach the optimal mean absolute error which was lower than 10 malaria cases (per 1,000 population at risk) in the Long Short Time Memory model. However, public health crises caused by political problems should be analyzed by political indexes for more precise predictions.

Information on dengue virus strains in this study.