INTRODUCTION
Malaria is a parasitic disease caused by Plasmodium.1 As the WHO reported in 2020, there were ∼229 million malaria cases and 409,000 related deaths in 87 countries.2 Although great strides have been made in global malaria control,2 malaria risk areas have not changed significantly over the past half-century.3 On the basis of previous research, 2.5 billion people are at risk for Plasmodium vivax malaria worldwide,4 whereas 1.13 and 1.44 billion people in the world are at risk for unstable and stable Plasmodium falciparum malaria, respectively.5 In particular, the retransmission of malaria resulting from imported cases has been reported in several countries, such as the United States, South Korea, and Italy, after the announcement of malaria elimination,6 raising public concerns about the possible resurgence of the disease.7,8 Related studies have been implemented in other southern European countries, and the results have shown that the resurgence risk is high.9–11
China was once a major endemic area for malaria. The country has achieved great success in the prevention and control of the disease with a WHO certification of malaria elimination issued in June 2021.12,13 However, imported cases occur in all provinces,14–16 and the number of imported malaria cases has shown an increasing trend year by year.16 An analysis of imported malaria in southeastern China indicates that the proportion of falciparum malaria leading to severe outcomes reached 76.3% (765/1,003) of the total malaria cases during the period 2012–2016, which is the predominant malaria with a serious health threat to the population.17 The vector Anopheles sinensis is widely distributed,18 and the population is generally susceptible to Plasmodium.19 In other words, a complete malaria transmission chain still exists that causes secondary transmission in China.20 Thus, how to identify hot spots for malaria reemergence in focus areas and develop a generic model for retransmitted malaria has gradually become the key and future focus of malaria prevention in China.21,22
Variability and transmission of malaria are greatly influenced by environmental changes.18,23,24 Multiple variables, including precipitation, temperature, altitude, and population, can affect the transmission process of malaria through sources of infection, transmission routes, and susceptible individuals.3,25 To evaluate the risk of malaria transmission in central Italy, Romi et al. combined a multifactorial approach with climatic parameters.6 The findings from Lee et al. demonstrated an increased risk of imported malaria in Asian-born populations in Minnesota.26 Previous studies also showed that vector An. sinensis spatial distribution was associated with malaria risk distribution.27–29 However, few studies were conducted to identify the broader environmental conditions for vector Anopheles and malaria transmission after the elimination of malaria.24,29,30
Applying various methods to model the spatial distribution of vector species can assist in the assessment and management of associated health risks.31 Studies using different modeling techniques to quantify the mathematical and physical relationships between diseases and the environment for the prediction of possible distribution are relatively widespread.32–35 Although both linear36 and nonlinear models3,18,37,38 have been used in previous studies, Song et al. observed that generalized addictive models performed better than linear models in spatial distribution estimation for malaria.3
Species distribution models are pivotal tools for forecasting and comprehending species distributions and have successfully identified the risk distribution of a disease in an area by changing environmental variables, including a dozen algorithms.39–41 Messina et al. used the boosted regression tree to predict the distribution of dengue and population at risk.42 Manyangadzer et al. used the negative binomial generalized linear mixed model to predict the spatial distribution of schistosomiasis infections.43 In addition, an ensemble forecast technique has been used to account for the variability among various algorithms to obtain the central tendency.44 The ensemble model combines several modeling approaches into a single predictive model to decrease variance and bias and improve prediction considerably.45–47
Shanghai is a high-risk area for malaria transmission. In this study, we focused on the spatial risk of the local malaria transmission resulted from the imported malaria patients as the source of infection. We constructed 10 algorithms and developed ensemble models to determine hierarchically the risk distributions for vector An. sinensis and malaria transmission and their environmental predictors. We also examined the association of risk classes between An. sinensis and malaria transmission risk classifications. The results of our study will help carry out precise prevention and consolidate the achievements of malaria control to meet the requirements of the WHO Global Vector Control Response (2017–2030).2
MATERIALS AND METHODS
Study area.
Shanghai was an endemic area for malaria with an annual incidence rate of more than 3% in the 1950s.14 Located in the Yangtze River Delta in eastern China between 120°52′ and 122°12′ E longitude and 30°40′ and 31°53′ N latitude, Shanghai currently has approximately 26 million people living within 63.405 million km2 and is one of the most densely populated areas in the world. It has a subtropical monsoon climate with a mild and humid environment, which is suitable for Anopheles breeding.48 The region has increasing trends of trade exchanges, high population density, and imported malaria cases.
Occurrence data.
We conducted an extensive literature search by using the keywords “Anopheles sinensis,” and the full text included “Shanghai” in China National Knowledge Infrastructure, Wanfang Database, and Weipu Database between 2010 and 2020. Review and experimental articles were excluded. A comprehensive literature review containing the title of the literature, the species of An. sinensis, and the occurrence records with time and location for An. sinensis was conducted. We collected all occurrence records in Shanghai from the comprehensive literature review, Global Biodiversity Information Facility database (GBIF, http://www.gbif.org/), and occurrences of An. sinensis found in normative field surveillance conducted by Jiading District Center for Disease Control and Prevention in the Jiading District of Shanghai in 2020. Then, a raw database containing occurrence records for An. sinensis was formed. Data from 451 malaria cases in Shanghai from 2010 to 2020 extracted from the Chinese National Disease Surveillance Reporting System were used to build the raw database for malaria.
We extracted all available location information for each occurrence in the two raw databases. Google Earth (https://www.google.com/earth/) was used to acquire the coordinates of collection points when the information was not provided. The occurrences with missing geospatial information and absent environmental variable layers were excluded. Duplicate records were removed, and only one presence point per grid (1 km × 1 km) was retained to reduce spatial autocorrelation.49–51 Figure 1 shows the presence of malaria and An. sinensis for modeling in our study.
Geographic distributions of Anopheles sinensis and malaria cases.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
Environmental variables.
We acquired climatic, geographic, and vegetation data including average annual temperature (AAT), average annual precipitation (AAP), ≥ 0°C annual accumulated temperature (AAT0DEM), ≥ 10°C annual accumulated temperature (AAT10DEM), aridity, moisture index (IM), elevation, aspect, normalized difference vegetation index (NDVI) from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn). We obtained host data, including human footprint index, human influence index, and night light index (NLI) through the International Center for Earth Science Information Network (http://sedac.ciesin.columbia.edu) and the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn). The raster data of the preceding 12 environmental variables were unified into 1 km × 1 km resolution. In addition, we unified the extent of Shanghai in all maps by using ArcGIS 10.2.
To avoid the effects of overparameterization and multicollinearity among variables,47 we calculated Pearson correlation coefficients among variables (Figure 2) and removed variables with high a correlation (r > 0.9).34 AAP, AAT0DEM, and Human Index were excluded. In addition to the nine environmental variables that were applied in the An. sinensis prediction model, the predicted risk distribution for An. sinensis was incorporated into the malaria prediction model (Table 1).
Pearson correlation analysis of variables. AAP = average annual precipitation; AAT = average annual temperature; AAT0DEM = ≥ 0°C annual accumulated temperature; AAT10DEM = ≥ 10°C annual accumulated temperature; DEM = elevation; IM = moisture index; NDVI = normalized difference vegetation index; NLI = night light index.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
Variables involved in model construction
| Descriptor type | Abbreviation | Variable description | Period | Model type |
|---|---|---|---|---|
| Climate | AAT | Average annual temperature | 2010–2015 | Anopheles sinensis, malaria |
| AAT10DEM | ≥ 10°C annual accumulated temperature | 1950–1990 | An. sinensis malaria | |
| Aridity | Aridity | 1950–1990 | An. sinensis, malaria | |
| IM | Moisture index | 1950–1990 | An. sinensis, malaria | |
| Geography | DEM | Elevation | 2000 | An. sinensis, malaria |
| Aspect | Aspect | 2000 | An. sinensis, malaria | |
| Vegetation | NDVI | Normalized differential vegetation index | 2010–2020 | An. sinensis, malaria |
| Host | Human Footprint | Human footprint index | 1995–2004 | An. sinensis, malaria |
| NLI | Night light index | 2000–2013 | An. sinensis, malaria | |
| Vector | ARD | An. sinensis risk distribution | 2010–2020 | Malaria |
Model development and assessment.
The biomod2 package of the R 4.1.0 was used to construct models with 10 algorithms: general linear models, general boosted models (GBM, also referred to as boosted regression trees), general additive models (GAM), classification tree analysis (CTA), artificial neural networks (ANN), surface range envelope (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forests (RF), and maximum entropy (MAXENT).52 We introduced pseudo-missing records by 1:1 random sampling for some models32 that required information that was missing. Figure 1 shows the pseudo-absence points of malaria and An. sinensis for modeling in our study.
Data of all the points in the original datasets were randomly split into two parts, with 75% calibrating the model (training data) and 25% evaluating the model (testing data). To reduce overfitting, we repeated the process of dividing the data, calibrating, and evaluating the model 10 times and obtained 100 models in total (10 modeling algorithms × 10 repeated runs). Although the ratio of the testing and training data in the original datasets remained the same, the testing data and training data differed in each run. Receiver operating characteristic curve (area under the curve [AUC]) and the kappa statistic were used to evaluate and compare synthetically the out-of-sample performance of the models on the testing data. The ability of the model to distinguish between occupied and unoccupied sites was evaluated by AUC. Also, the kappa statistic was used to evaluate model consistency.47 When evaluating kappa, the models transformed the predictions into binary predictive maps with a threshold when the kappa value was maximal. A high value close to 1.0 reflects a strong signal of an excellent model performance.53 Models with AUC > 0.7 and kappa > 0.4 were weighted by AUC to develop ensemble models.6,54–56 We also calculated the ranks for the importance of environmental variables to compare the out-of-sample performance among models.
Limited by the small scale of Shanghai and by the spatial resolution of the variables, the predictive raster layers created by the ensemble models for An. sinensis and malaria transmission were composed of the 1 km × 1 km grids. To minimize grid-to-grid contact surface differences and make logical transitions to improve the resolution of raster layers for viewing, we used Kriging interpolation over the raster layers created by the ensemble models.57,58 Further, to compare the impact on the raster layers before and after Kriging interpolation, fuzzy kappa implemented in the Map Comparison Kit version 3.2.3 was used to compare these raster layers with unequal resolution per grid statistically.59 Between 0 (completely different maps) and 1 (identical maps), the fuzzy kappa indicates the average agreement between two maps compared with the expected agreement from random relocation of all grid squares in two maps.59–61 The values of fuzzy kappa after Kriging interpolation were 0.939 for An. sinensis mapping and 0.942 for malaria transmission mapping with excellent consistency, and the differences of all the grid squares caused by Kriging interpolation are shown in Supplemental Figure 1.
Risk classification.
The ensemble models were ultimately selected to draw the risk classification maps of An. sinensis and malaria transmission. The final output predictions from the ensemble models had a range of values from 0 to 1. These values as the variable reflected the likelihood of occurrence, which were regrouped into four risk classes: no risk (0.00–0.40), low risk (0.41–0.60), intermediate risk (0.61–0.80), and high risk (0.81–1.00).
In addition, by using ArcGIS 10.2 to compare the consistency of risk classes for the output predictions of An. sinensis and malaria transmission per grid, we examined the risk class association by assessing whether the risk levels of the areas in both maps were the same.
RESULTS
Model out-of-sample performance.
Figure 3 shows the out-of-sample performance of 10 algorithms. Random forests (An. sinensis: AUC = 0.827, kappa = 0.632) and FDA (AUC = 0.935, kappa = 0.750) for malaria had the best overall out-of-sample performance, and SRE (AUC = 0.395, kappa = 0.010) for An. sinensis and MAXENT (AUC = 0.450, kappa = 0.010) for malaria had the worst overall out-of-sample performance.
Out-of-sample performance in terms of area under the curve (AUC) and kappa over the 100 predictive models (10 algorithms × 10 repeated runs). ANN = artificial neural networks; CTA = classification tree analysis; FDA = flexible discriminant analysis; GAM = general additive models; GBM = general boosted models; GLM = general linear models; MARS = multiple adaptive regression splines; MAXENT = maximum entropy; RF = random forests; SRE = surface range envelope.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
The An. sinensis ensemble model was obtained from 13 models with AUC > 0.7 and kappa > 0.4 (three models using RF or GBM algorithm, two models using FDA, and one model MAXENT, MARS, CTA, ANN, or GAM algorithm), and the malaria ensemble model was obtained from 85 models including all 10 algorithms. Compared with any single model, the out-of-sample performance of the ensemble models was best (An. sinensis: AUC = 0.981, kappa = 0.920 and malaria: AUC = 0.959, kappa = 0.800), and was applied to classification mapping.
Importance analysis of environmental predictors.
Figure 4 shows the ranked importance of environmental predictors of different algorithms in the final ensemble models. For An. sinensis, IM, as an indicator of the degree of wetness, was the most important environmental predictor in the ensemble model. The higher the degree of wetness, the lower the risk of An. sinensis. Average annual temperature and AAT10DEM, as indicators of average temperatures, also ranked high in some models.
Ranking of importance of the contribution of each variable of different algorithms in the final ensemble models. The length of the vertical bar indicated the difference in the rank of importance for the same variable in the above algorithms. AAT = average annual temperature; AAT10DEM = ≥ 10°C annual accumulated temperature; ANN = artificial neural networks; ARD = Anopheles sinensis risk distribution; CTA = classification tree analysis; DEM = elevation; FDA = flexible discriminant analysis; GAM = general additive models; GBM = general boosted models; IM = moisture index; MARS = multiple adaptive regression splines; MAXENT = maximum entropy; NDVI = normalized difference vegetation index; NLI = night light index; RF = random forests; SRE = surface range envelope.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
For malaria transmission, NDVI, as an indicator of the vegetation cover status, showed the highest importance in the ensemble model; the risk of malaria transmission decreased with greater vegetation cover. NLI—that is, the brightness of night light, which reflects socioeconomic conditions—and Human Footprint, as the quantitative indicator of the impact of human activity, also ranked high in some models.
Prediction of An. sinensis risk distribution in Shanghai.
Anopheles sinensis risk areas were found in all districts of Shanghai, with high-risk areas concentrated in the northern part of the city (Figure 5). The high risk areas of An. sinensis were mainly located in the Jiading and Baoshan districts, showing a certain aggregation. Other districts, such as Putuo, Jingan, and Hongkou, also had a small number of high-risk areas for An. sinensis.
Classification map of Anopheles sinensis risk in Shanghai predicted by the ensemble model.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
Classification of malaria cases in Shanghai.
Eighty unique spatial locations in Shanghai were identified from 451 malaria cases. The locations of reported malaria cases were distributed in all the 16 districts of Shanghai, mainly in central and northern Shanghai. As a result, 73.8% (59/80) of the reported malaria cases were located in An. sinensis risk areas, and 26.2% (21/80) in no risk areas of An. sinensis (Figure 6).
Classification of malaria cases in Shanghai based on the classification map of Anopheles sinensis risk.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
Prediction of malaria transmission risk in Shanghai.
Risk areas of malaria transmission were distributed in all the districts and mainly concentrated in central Shanghai (Figure 7). Among them, the Pudong district had the largest transmission risk area for malaria. The high-risk areas showed aggregation in the city center covering 11 districts. For example, Pudong had the largest high-risk area, followed by Putuo, Jingan, Hongkou, and other districts in the city center. In contrast, there were few high-risk areas in the far suburbs of the city.
Classification map of malaria transmission risk in Shanghai predicted by the ensemble model.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
Risk classification mismatch between malaria transmission and An. sinensis.
There was a lack of strong risk class association between the risk distributions of malaria transmission and An. sinensis (Figure 8). Comparing the two predictive risk maps of malaria transmission and An. sinensis, the overlap of risk classification was only 3,410.9 km2 (53.8%). Among the areas with mismatched classification, the higher risk level for malaria transmission was 1,024.7 km2 (16.1%), and the higher risk level for An. sinensis was 1,908.4km2 (30.1%).
Map of the risk classification overlap between Anopheles sinensis and malaria.
Citation: The American Journal of Tropical Medicine and Hygiene 108, 3; 10.4269/ajtmh.22-0523
DISCUSSION
The study provided hierarchical maps of risk distributions for the vector An. sinensis and malaria transmission predicted by ensemble models with multiple modeling algorithms. The risk areas of both An. sinensis and malaria transmission were found to be mainly concentrated in the north and central areas, which required attention. Currently, there are still some high-risk areas that lack routine surveillance for An. sinensis, such as the Chongming and Baoshan districts.
Only 73.4% (59/80) of reported malaria cases were located in An. sinensis risk areas (Figure 6), suggesting that vector monitoring at a finer scale is not enough. We compared the two predictive risk classification maps and found that the risk distributions of malaria transmission and An. sinensis did not correspond to each other (Figure 8). Similar results have been observed in Europe, indicating that autochthonous transmission is low despite the documented presence of Anopheles.62 A similar mismatch was also found for the risk distributions of Chagas disease and T. infestans.63 A possible reason is that for Shanghai, where the malaria elimination goal has been achieved, host factors, such as the sites of imported malaria cases and NLI, played a greater role than presence of the vector An. sinensis. This may partly explain the resurgence of malaria in other countries where mosquito surveillance is incomplete.6,64–66 The mismatch of risk classifications between vector and disease indicates that the prevention of malaria resurgence should not over-rely on routine surveillance of local Anopheles.
Risk classification could be considered in malaria prevention. Lingala et al. proposed that classified rainfall could help provide early warning of impending malaria outbreaks.67 Stoler et al. also classified the distance for the effect of urban agriculture on self-reported malaria in Ghana.68 In addition to assessing the relationship between environmental variables and malaria transmission, classification helps control the outbreak risk of An. sinensis and malaria transmission. Harvey et al. developed the first malaria epidemic early warning system and using classifications that meet the conditions.69 They further tested this system and discovered that for the high alert threshold, precision increased to > 99% and recall to 5%. In fact, several species, including the Oncomelania hupensis, Aedes albopictus, and Culicoides, have successfully used the output predictions from similar models for risk classification, which could contribute to the development of effective strategies to prevent further spread.47,49,70 Similarly, the risk classification was based on the values of the output predictions from the ensemble models in our study. A high-risk class indicates high environmental suitability of the species in this area. By identifying where the risk class is high, we can determine the hotspots for the species in an area, which could provide early warning of An. sinensis and malaria transmission. It did not ensure that An. sinensis or malaria transmission occurs in high-risk or risk areas but rather provided possible specific guidance on priorities for the prevention and control of An. sinensis and malaria transmission. Our classification maps indicate some high-risk areas where prevention and control could be strengthened.
The risk distributions of malaria transmission and presence of An. sinensis were influenced by multiple factors. IM and AAT greatly influenced the risk distribution for An. sinensis in ensemble models, which is consistent with results from previous studies.71,72 Host factors such as the NLI and Human Footprint strongly influenced the risk distribution for malaria transmission in ensemble models. With the expansion of the population in Shanghai due to economic development and urban expansion, the growth in susceptible populations will further lay the groundwork for the resurgence of malaria.
The ensemble model performed best in our study. For a single model, the out-of-sample performance of RF and FDA was best, and SRE and MAXENT were worst. Although MAXENT has been used in some studies to predict malaria distribution,18,39,73 its out-of-sample performance was less ideal in our study. Further, studies have shown that a single model did not perform well under various conditions,35,46,74,75 which influences the effectiveness of model predictions.44,76 Indeed, the results of the application of the traditional method that selected the best from multiple models often differ from actual observations, especially when the observation set is spatially or temporally independent from the calibration set.77,78 In our study, the ensemble modeling technique was applied to deal with intermodel uncertainty52,79 and was more robust and accurate in prediction.
There are some limitations in our study. The model used geographic locations but did not consider An. sinensis density, which plays a crucial role in disease transmission.49 Further, the An. sinensis locations from literature differed in sampling and resolution, where spatial autocorrelation might exist. For this, we supplemented occurrences for An. sinensis found in the normative field surveillance and occurrences in the GBIF database to the raw database. Although not all environmental variables were available for the 2010–2020 period with enough resolution, we tried to use stable variables that cover or approach this period and interpolation analysis to improve the resolution of the raster layer created by the ensemble models for An. sinensis and malaria transmission. Also, fuzzy kappa was used to measure the differences caused by interpolation per grid statistically. However, temporal effects caused by different years of occurrence and variables might be limitations for this kind of study for predicting risk distribution by variables, which has been identified in previous studies.32,47 Finally, only locations, where the malaria cases were located at the time of diagnosis, were included in our study. Theoretically, all locations experienced by a patient during the infectious period are at spatial risk of transmission at the same time and should be considered.73,80
In conclusion, we established ensemble models combining multiple algorithms that better predicted the risk distributions for An. sinensis and malaria transmission in Shanghai. Environmental predictors represented by climate and host factors indicated strong importance among different model algorithms. In Shanghai, risk areas for An. sinensis and malaria transmission varied in size and level in the 16 districts, but there was not a strong relationship between An. sinensis and malaria risk classification. Beyond precise An. sinensis monitoring in transmission risk areas of malaria, the challenge of malaria in Shanghai asks for the focus of malaria surveillance even in low-risk areas for An. sinensis.
Supplemental Materials
REFERENCES
- 1.↑
Lai S et al., 2017. Malaria in China, 2011–2015: an observational study. Bull World Health Organ 95: 564–573.
- 2.↑
WHO , 2020. World Malaria Report 2020: 20 Years of Global Progress and Challenges. Geneva, Switzerland: World Health Organization.
- 3.↑
Song Y , Ge Y , Wang J , Ren Z , Liao Y , Peng J , 2016. Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050. Malar J 15: 345.
- 4.↑
Gething PW et al., 2012. A long neglected world malaria map: Plasmodium vivax endemicity in 2010. PLoS Negl Trop Dis 6: e1814.
- 5.↑
Gething PW , Patil AP , Smith DL , Guerra CA , Elyazar IR , Johnston GL , Tatem AJ , Hay SI , 2011. A new world malaria map: Plasmodium falciparum endemicity in 2010. Malar J 10: 378.
- 6.↑
Romi R et al., 2012. Assessment of the risk of malaria re-introduction in the Maremma plain (central Italy) using a multi-factorial approach. Malar J 11: 98.
- 7.↑
Harrus S , Baneth G , 2005. Drivers for the emergence and re-emergence of vector-borne protozoal and bacterial diseases. Int J Parasitol 35: 1309–1318.
- 8.↑
Rogers DJ , Randolph SE , 2000. The global spread of malaria in a future, warmer world. Science 289: 1763–1766.
- 9.↑
Sainz-Elipe S , Latorre JM , Escosa R , Masià M , Fuentes MV , Mas-Coma S , Bargues MD , 2010. Malaria resurgence risk in southern Europe: climate assessment in an historically endemic area of rice fields at the Mediterranean shore of Spain. Malar J 9: 221.
- 10.↑
Faraj C , Ouahabi S , Adlaoui E , Boccolini D , Romi R , El Aouad R , 2008. Assessment of malaria resurgence risk in Morocco. Study of the vectorial capacity of Anopheles labranchiae in a rice cultivation area in the north of the country. Parasite 15: 605–610.
- 11.↑
Di Luca M , Boccolini D , Severini F , Toma L , Barbieri FM , Massa A , Romi R , 2009. A 2-year entomological study of potential malaria vectors in central Italy. Vector Borne Zoonotic Dis 9: 703–711.
- 12.↑
Zhang L , Zhou SS , Feng J , Fang W , Xia ZG , 2015. Malaria situation in the People’s Republic of China in 2014. Zhongguo Ji Sheng Chong Xue Yu Ji Sheng Chong Bing Za Zhi 33: 319–326.
- 13.↑
Feng X , Zhang S , Huang F , Zhang L , Feng J , Xia Z , Zhou H , Hu W , Zhou S , 2017. Biology, bionomics and molecular biology of Anopheles sinensis Wiedemann 1828 (Diptera: Culicidae), main malaria vector in China. Front Microbiol 8: 1473.
- 14.↑
Feng J , Xia ZG , Vong S , Yang WZ , Zhou SS , Xiao N , 2014. Preparedness for malaria resurgence in China: case study on imported cases in 2000–2012. Adv Parasitol 86: 231–265.
- 15.↑
Zhang Q et al., 2014. The epidemiology of Plasmodium vivax and Plasmodium falciparum malaria in China, 2004–2012: from intensified control to elimination. Malar J 13: 419.
- 16.↑
Zhou S , Li Z , Cotter C , Zheng C , Zhang Q , Li H , Zhou S , Zhou X , Yu H , Yang W , 2016. Trends of imported malaria in China 2010–2014: analysis of surveillance data. Malar J 15: 39.
- 17.↑
Zhang X , Yao L , Sun J , Pan J , Chen H , Zhang L , Ruan W , 2018. Malaria in southeastern China from 2012 to 2016: analysis of imported cases. Am J Trop Med Hyg 98: 1107–1112.
- 18.↑
Ren Z et al., 2016. Predicting malaria vector distribution under climate change scenarios in China: challenges for malaria elimination. Sci Rep 6: 20604.
- 19.↑
Mendonça VR , Queiroz AT , Lopes FM , Andrade BB , Barral-Netto M , 2013. Networking the host immune response in Plasmodium vivax malaria. Malar J 12: 69.
- 20.↑
Tatem AJ , Jia P , Ordanovich D , Falkner M , Huang Z , Howes R , Hay SI , Gething PW , Smith DL , 2017. The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics. Lancet Infect Dis 17: 98–107.
- 21.↑
Zhang SS , Feng J , Zhang L , Ren X , Geoffroy E , Manguin S , Frutos R , Zhou SS , 2019. Imported malaria cases in former endemic and non-malaria endemic areas in China: are there differences in case profile and time to response? Infect Dis Poverty 8: 61.
- 22.↑
Li Z et al., 2016. Epidemiologic features of overseas imported malaria in the People’s Republic of China. Malar J 15: 141.
- 23.↑
Loiseau C , Harrigan RJ , Bichet C , Julliard R , Garnier S , Lendvai AZ , Chastel O , Sorci G , 2013. Predictions of avian Plasmodium expansion under climate change. Sci Rep 3: 1126.
- 24.↑
Caminade C , Kovats S , Rocklov J , Tompkins AM , Morse AP , Colón-González FJ , Stenlund H , Martens P , Lloyd SJ , 2014. Impact of climate change on global malaria distribution. Proc Natl Acad Sci USA 111: 3286–3291.
- 25.↑
Liu J , Chen XP , 2006. Relationship of remote sensing normalized differential vegetation index to Anopheles density and malaria incidence rate. Biomed Environ Sci 19: 130–132.
- 26.↑
Lee EH , Miller RH , Masuoka P , Schiffman E , Wanduragala DM , DeFraites R , Dunlop SJ , Stauffer WM , Hickey PW , 2018. Predicting risk of imported disease with demographics: geospatial analysis of imported malaria in Minnesota, 2010–2014. Am J Trop Med Hyg 99: 978–986.
- 27.↑
Foley DH , Torres EP , Mueller I , Bryan JH , Bell D , 2003. Host-dependent Anopheles flavirostris larval distribution reinforces the risk of malaria near water. Trans R Soc Trop Med Hyg 97: 283–287.
- 28.↑
Minakawa N , Seda P , Yan G , 2002. Influence of host and larval habitat distribution on the abundance of African malaria vectors in western Kenya. Am J Trop Med Hyg 67: 32–38.
- 29.↑
Zhou SS , Zhang SS , Wang JJ , Zheng X , Huang F , Li WD , Xu X , Zhang HW , 2012. Spatial correlation between malaria cases and water-bodies in Anopheles sinensis dominated areas of Huang-Huai plain, China. Parasit Vectors 5: 106.
- 30.↑
Gething PW , Smith DL , Patil AP , Tatem AJ , Snow RW , Hay SI , 2010. Climate change and the global malaria recession. Nature 465: 342–345.
- 31.↑
Hongoh V , Hoen AG , Aenishaenslin C , Waaub JP , Bélanger D , Michel P , 2011. Spatially explicit multi-criteria decision analysis for managing vector-borne diseases. Int J Health Geogr 10: 70.
- 32.↑
Leta S , Fetene E , Mulatu T , Amenu K , Jaleta MB , Beyene TJ , Negussie H , Revie CW , 2019. Modeling the global distribution of Culicoides imicola: an Ensemble approach. Sci Rep 9: 14187.
- 33.↑
Obenauer JF , Andrew Joyner T , Harris JB , 2017. The importance of human population characteristics in modeling Aedes aegypti distributions and assessing risk of mosquito-borne infectious diseases. Trop Med Health 45: 38.
- 34.↑
Yang Y et al., 2018. Prediction of the potential global distribution for Biomphalaria straminea, an intermediate host for Schistosoma mansoni. PLoS Negl Trop Dis 12: e0006548.
- 35.↑
Dagtekin D , Şahan EA , Denk T , Köse N , Dalfes HN , 2020. Past, present and future distributions of Oriental beech (Fagus orientalis) under climate change projections. PLoS One 15: e0242280.
- 36.↑
Nihei N , Hashida Y , Kobayashi M , Ishii A , 2002. Analysis of malaria endemic areas on the Indochina Peninsula using remote sensing. Jpn J Infect Dis 55: 160–166.
- 37.↑
Midekisa A , Senay G , Henebry GM , Semuniguse P , Wimberly MC , 2012. Remote sensing-based time series models for malaria early warning in the highlands of Ethiopia. Malar J 11: 165.
- 38.↑
Wimberly MC , Midekisa A , Semuniguse P , Teka H , Henebry GM , Chuang TW , Senay GB , 2012. Spatial synchrony of malaria outbreaks in a highland region of Ethiopia. Trop Med Int Health 17: 1192–1201.
- 39.↑
Valderrama L , Ayala S , Reyes C , González CR , 2021. Modeling the potential distribution of the malaria vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in arid regions of northern Chile. Front Public Health 9: 611152.
- 40.↑
Aguiar BS , Lorenz C , Virginio F , Suesdek L , Chiaravalloti-Neto F , 2018. Potential risks of Zika and chikungunya outbreaks in Brazil: a modeling study. Int J Infect Dis 70: 20–29.
- 41.↑
Thuiller W , Pollock LJ , Gueguen M , Münkemüller T , 2015. From species distributions to meta-communities. Ecol Lett 18: 1321–1328.
- 42.↑
Messina JP et al., 2019. The current and future global distribution and population at risk of dengue. Nat Microbiol 4: 1508–1515.
- 43.↑
Manyangadze T , Chimbari MJ , Rubaba O , Soko W , Mukaratirwa S , 2021. Spatial and seasonal distribution of Bulinus globosus and Biomphalaria pfeifferi in Ingwavuma, uMkhanyakude district, KwaZulu-Natal, South Africa: implications for schistosomiasis transmission at micro-geographical scale. Parasit Vectors 14: 222.
- 44.↑
Araújo MB , New M , 2007. Ensemble forecasting of species distributions. Trends Ecol Evol 22: 42–47.
- 45.↑
Bellard C , Thuiller W , Leroy B , Genovesi P , Bakkenes M , Courchamp F , 2013. Will climate change promote future invasions? Glob Change Biol 19: 3740–3748.
- 46.↑
Lei J , Chen L , Li H , 2017. Using ensemble forecasting to examine how climate change promotes worldwide invasion of the golden apple snail (Pomacea canaliculata). Environ Monit Assess 189: 404.
- 47.↑
Xia C et al., 2019. Identification of high-risk habitats of Oncomelania hupensis, the intermediate host of Schistosoma japonium in the Poyang Lake region, China: a spatial and ecological analysis. PLoS Negl Trop Dis 13: e0007386.
- 48.↑
Abiodun GJ , Maharaj R , Witbooi P , Okosun KO , 2016. Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis. Malar J 15: 364.
- 49.↑
Echeverry-Cárdenas E , López-Castañeda C , Carvajal-Castro JD , Aguirre-Obando OA , 2021. Potential geographic distribution of the tiger mosquito Aedes albopictus (Skuse, 1894) (Diptera: Culicidae) in current and future conditions for Colombia. PLoS Negl Trop Dis 15: e0008212.
- 50.↑
Veloz SD , 2010. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J Biogeogr 36: 2290–2299.
- 51.↑
Zhang K , Yao L , Meng J , Tao J , 2018. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci Total Environ 634: 1326–1334.
- 52.↑
Thuiller WLB , Engler R , Araújo MB , 2009. BIOMOD – a platform for ensemble forecasting of species distributions. Ecography 32: 369–373.
- 53.↑
Allouche O , Tsoar A , Kadmon R , 2010. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43: 1223–1232.
- 54.↑
Chalghaf B , Chlif S , Mayala B , Ghawar W , Bettaieb J , Harrabi M , Benie GB , Michael E , Salah AB , 2016. Ecological niche modeling for the prediction of the geographic distribution of cutaneous leishmaniasis in Tunisia. Am J Trop Med Hyg 94: 844–851.
- 55.↑
Landis JR , Koch GG , 1977. The measurement of observer agreement for categorical data. Biometrics 33: 159–174.
- 56.↑
Marmion M , Parviainen M , Luoto M , Heikkinen RK , Thuiller W , 2010. Evaluation of consensus methods in predictive species distribution modelling. Divers Distrib 15: 59–69.
- 57.↑
Oliver MA , 1990. Kriging: a method of interpolation for geographical information systems. Int J Geogr Inf Syst 4: 313–332.
- 58.↑
Rebholz B , Almekkawy M , 2020. Efficacy of kriging interpolation in ultrasound imaging; subsample displacement estimation. Annu Int Conf IEEE Eng Med Biol Soc 2020: 2137–2141.
- 60.↑
Hagen-Zanker A , 2009. An improved fuzzy kappa statistic that accounts for spatial autocorrelation. Int J Geogr Inf Sci 23: 61–73.
- 61.↑
Arenas-Castro S , Gonçalves J , Alves P , Alcaraz-Segura D , Honrado JP , 2018. Assessing the multi-scale predictive ability of ecosystem functional attributes for species distribution modelling. PLoS One 13: e0199292.
- 62.↑
Piperaki ET , Daikos GL , 2016. Malaria in Europe: emerging threat or minor nuisance? Clin Microbiol Infect 22: 487–493.
- 63.↑
Tapia-Garay V et al., 2018. Assessing the risk zones of Chagas’ disease in Chile, in a world marked by global climatic change. Mem Inst Oswaldo Cruz 113: 24–29.
- 64.↑
Murindahabi MM et al., 2021. Monitoring mosquito nuisance for the development of a citizen science approach for malaria vector surveillance in Rwanda. Malar J 20: 36.
- 65.↑
Bergmann C et al., 2021. Increase in Kelch 13 polymorphisms in Plasmodium falciparum, southern Rwanda. Emerg Infect Dis 27: 294–296.
- 66.↑
Murray CJ et al., 2014. Global, regional, and national incidence and mortality for HIV, tuberculosis, and malaria during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384: 1005–1070.
- 67.↑
Lingala MAL , Singh P , Verma P , Dhiman RC , 2020. Determining the cutoff of rainfall for Plasmodium falciparum malaria outbreaks in India. J Infect Public Health 13: 1034–1041.
- 68.↑
Stoler J , Weeks JR , Getis A , Hill AG , 2009. Distance threshold for the effect of urban agriculture on elevated self-reported malaria prevalence in Accra, Ghana. Am J Trop Med Hyg 80: 547–554.
- 69.↑
Harvey D , Valkenburg W , Amara A , 2021. Predicting malaria epidemics in Burkina Faso with machine learning. PLoS One 16: e0253302.
- 70.↑
Cuéllar AC et al., 2018. Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance. Parasit Vectors 11: 608.
- 71.↑
Stoops CA , Gionar YR , Shinta, Sismadi P , Elyazar IR , Bangs MJ , Sukowati S , 2007. Environmental factors associated with spatial and temporal distribution of Anopheles (Diptera: Culicidae) larvae in Sukabumi, West Java, Indonesia. J Med Entomol 44: 543–553.
- 72.↑
Ciota AT , Chin PA , Ehrbar DJ , Micieli MV , Fonseca DM , Kramer LD , 2018. Differential effects of temperature and mosquito genetics determine transmissibility of arboviruses by Aedes aegypti in Argentina. Am J Trop Med Hyg 99: 417–424.
- 73.↑
Gomes E , Capinha C , Rocha J , Sousa C , 2016. Mapping risk of malaria transmission in mainland Portugal using a mathematical modelling approach. PLoS One 11: e0164788.
- 74.↑
Lei Y , Liu Q , 2021. Tolerance niche expansion and potential distribution prediction during Asian openbill bird range expansion. Ecol Evol 11: 5562–5574.
- 75.↑
Deka MA , Morshed N , 2018. Mapping disease transmission risk of Nipah virus in South and Southeast Asia. Trop Med Infect Dis 3: 57.
- 76.↑
Thuiller W , 2014. Editorial commentary on “BIOMOD—optimizing predictions of species distributions and projecting potential future shifts under global change.” Glob Change Biol 20: 3591–3592.
- 77.↑
Araújo M , Thuiller W , Williams P , Reginster I , 2005. Downscaling European species atlas distributions to a finer resolution. Global Ecol Biogeogr 14: 17–30.
- 78.↑
Araújo MB , Pearson RG , Thuiller W , Erhard M , 2005. Validation of species–climate impact models under climate change. Glob Change Biol 11: 1504–1513.
- 79.↑
Guo Q , Liu Y , 2010. ModEco: an integrated software package for ecological niche modeling. Ecography 33: 637–642.
- 80.↑
Wilkinson RR , Sharkey KJ , 2018. Impact of the infectious period on epidemics. Phys Rev E 97: 052403.