Spatial analysis and mapping of malaria risk in an endemic area, south of Iran: a GIS based decision making for planning of control

Evaluation of the knockdown resistance locus (kdr) in Anopheles stephensi (Diptera: Culicidae) in southeastern Iran

Background & objectives

The resistance to insecticide among Anopheles stephensi population due to insecticide selection pressure has been previously reported from Iran. The current study was performed to evaluate the susceptibility of different insecticide reagents against An. stephensi by bioassay and molecular methods in Saravan County, a malaria-endemic area in southeastern Iran.

Methods

An. stephensi mosquitoes were collected from different larval habitats in Saravan City, southeastern Iran in 2022. At first, the susceptibility of collected samples for DDT, permethrin, and deltamethrin were evaluated by bioassay test. The collected mosquitoes were then evaluated for the presence of different kdr mutations.

Results

Insecticide susceptibility tests were conducted on the field population of An. stephensi from Saravan, revealing its potential resistance to pyrethroids and DDT. Of the 150 An. stephensi samples, 4 % carried the kdr L1014F mutation as heterozygous and the rest of them were homozygous L1014 wild type.

Interpretation & conclusion

The current study revealed the presence of L1014F mutation for the first time in Iran. So, further monitoring of kdr mutations in the VGSC gene and resistance phenotypes should be performed.

Spatio-temporal Prediction of the Malaria Transmission Risk in Minab District (Hormozgan Province, Southern Iran)

INTRODUCTION

Malaria is the most important parasitic disease in tropical and subtropical regions, with more than 240 million cases reported annually. In Iran, indigenous cases occur in its south-eastern region. The aim of this study is to assess the environmental risk of malaria transmission in an endemic area of southern Iran.

METHODS

The study was carried out in Minab district (Hormozgan province, southern Iran), with the aim to assess the environmental risk of malaria, based on a spatio-temporal study, using Growing Degree Days (GDD)-based predictions, larval habitat ecology, MaxEnt spatial predictions and malaria transmission data.

RESULTS

The Gradient Model Risk index showed the highest malaria transmission risk period to be during January-April and October-December. The ecological conditions of water bodies of larval habitats of the four vector species (Anopheles culicifacies, A. dthali, A. fluviatilis and A. stephensi) were assessed, with A. stephensi being the most prevalent and the most widely distributed species.

CONCLUSION

These findings, together with the MaxEnt Anopheles predictive distribution models, allowed identifying villages in danger of malaria transmission in Minab district. This spatio-temporal prediction of malaria transmission risk should be incorporated in the design of malaria control initiatives towards a local malaria early warning system. Moreover, the proposed transmission risk model can be extrapolated, at local scale, to other malaria endemic areas of tropical and subtropical regions.

The Fauna, Diversity, and Bionomics of Culicinae (Diptera: Culicidae) in Hormozgan Province, Southern Iran

Hormozgan Province is one of the important foci of malaria in Iran. In addition to malaria pathogens, mosquitoes also transmit the pathogens that cause dirofilariasis and West Nile fever in the province. Also, the threat of emerging aedine-borne viruses that cause infections, such as Chikungunya, dengue, and Zika, is noticeable. There is little information about the fauna and bionomics of Culicinae in the province. The present investigation aimed to study the fauna, diversity, and bionomics of culicines. The study was conducted from September 2016 to April 2017 in four counties of Bandar Abbas, Bandar Khamir, Bashagard, and Jask. In total, 3,236 larvae and 1,901 adults including 16 culicine species were collected. The larvae of Culiseta longiareolata (Macquart) (25.65%), Culex pipiens Linnaeus (16.62%), and Cx. quinquefasciatus Say (16.16%) were most abundant and Cx. hortensis Ficalbi (0.09%) was least abundant. Among adults, Cx. laticinctus Edwards (33.19%), Cx. quinquefasciatus (31.09%), and Cx. pipiens (11.99%) were the most prevalent species and Cs. longiareolata (0.47%), Aedes caballus (Theobald) (0.90%), and Cx. bitaeniorhynchus Giles (0.90%) were the least prevalent species. The pairwise similarities of fauna of the counties and different collecting methods and diversity indices were investigated. More ecological data, especially on host preference, seasonality, and larval habitat characteristics, are needed as a basic knowledge for any intervention measures using integrated vector management.

Temporal Monitoring and Predicting of the Abundance of Malaria Vectors Using Time Series Analysis of Remote Sensing Data through Google Earth Engine

In many studies regarding the field of malaria, environmental factors have been acquired in single-time, multi-time or a short-time series using remote sensing and meteorological data. Selecting the best periods of the year to monitor the habitats of Anopheles larvae can be effective in better and faster control of malaria outbreaks. In this article, high-risk times for three regions in Iran, including Qaleh-Ganj, Sarbaz and Bashagard counties with a history of malaria prevalence were estimated. For this purpose, a series of environmental factors affecting the growth and survival of Anopheles were used over a seven-year period through the Google Earth Engine. The results of this study indicated two high-risk times for Qaleh-Ganj and Bashagard counties and three high-risk times for Sarbaz county over the course of a year observing an increase in the abundance of Anopheles mosquitoes. Further evaluation of the results against the entomological data available in previous studies showed that the high-risk times predicted in this study were consistent with an increase in the abundance of Anopheles mosquitoes in the study areas. The proposed method is extremely useful for temporal prediction of the increase in abundance of Anopheles mosquitoes in addition to the use of optimal data aimed at monitoring the exact location of Anopheles habitats.

A practical approach for geographic prioritization and targeting of insecticide-treated net distribution campaigns during public health emergencies and in resource-limited settings

BACKGROUND

The use of data in targeting malaria control efforts is essential for optimal use of resources. This work provides a practical mechanism for prioritizing geographic areas for insecticide-treated net (ITN) distribution campaigns in settings with limited resources.

METHODS

A GIS-based weighted approach was adopted to categorize and rank administrative units based on data that can be applied in various country contexts where Plasmodium falciparum transmission is reported. Malaria intervention and risk factors were used to rank local government areas (LGAs) in Nigeria for prioritization during mass ITN distribution campaigns. Each factor was assigned a unique weight that was obtained through application of the analytic hierarchy process (AHP). The weight was then multiplied by a value based on natural groupings inherent in the data, or the presence or absence of a given intervention. Risk scores for each factor were then summated to generate a composite unique risk score for each LGA. This risk score was translated into a prioritization map which ranks each LGA from low to high priority in terms of timing of ITN distributions.

RESULTS

A case study using data from Nigeria showed that a major component that influenced the prioritization scheme was ITN access. Sensitivity analysis results indicate that changes to the methodology used to quantify ITN access did not modify outputs substantially. Some 120 LGAs were categorized as 'extremely high' or 'high' priority when a spatially interpolated ITN access layer was used. When prioritization scores were calculated using DHS-reported state level ITN access, 108 (90.0%) of the 120 LGAs were also categorized as being extremely high or high priority. The geospatial heterogeneity found among input risk factors suggests that a range of variables and covariates should be considered when using data to inform ITN distributions.

CONCLUSION

The authors provide a tool for prioritizing regions in terms of timing of ITN distributions. It serves as a base upon which a wider range of vector control interventions could be targeted. Its value added can be found in its potential for application in multiple country contexts, expediated timeframe for producing outputs, and its use of systematically collected malaria indicators in informing prioritization.

Spatial distribution of Plasmodium knowlesi cases and their vectors in Johor, Malaysia: in light of human malaria elimination

Background

Plasmodium knowlesi, a simian malaria parasite infection, increases as Plasmodium falciparum and Plasmodium vivax infections decrease in Johor, Malaysia. Therefore, this study aimed to identify the distribution of vectors involved in knowlesi malaria transmission in Johor. This finding is vital in estimating hotspot areas for targeted control strategies.

Methods

Anopheles mosquitoes were collected from the location where P. knowlesi cases were reported. Cases of knowlesi malaria from 2011 to 2019 in Johor were analyzed. Internal transcribed spacers 2 (ITS2) and cytochrome c oxidase subunit I (COI) genes were used to identify the Leucosphyrus Group of Anopheles mosquitoes. In addition, spatial analysis was carried out on the knowlesi cases and vectors in Johor.

Results

One hundred and eighty-nine cases of P. knowlesi were reported in Johor over 10 years. Young adults between the ages of 20–39 years comprised 65% of the cases. Most infected individuals were involved in agriculture and army-related occupations (22% and 32%, respectively). Four hundred and eighteen Leucosphyrus Group Anopheles mosquitoes were captured during the study. Anopheles introlatus was the predominant species, followed by Anopheles latens. Spatial analysis by Kriging interpolation found that hotspot regions of P. knowlesi overlapped or were close to the areas where An. introlatus and An. latens were found. A significantly high number of vectors and P. knowlesi cases were found near the road within 0–5 km.

Conclusions

This study describes the distribution of P. knowlesi cases and Anopheles species in malaria-endemic transmission areas in Johor. Geospatial analysis is a valuable tool for studying the relationship between vectors and P. knowlesi cases. This study further supports that the Leucosphyrus Group of mosquitoes might be involved in transmitting knowlesi malaria cases in Johor. These findings may provide initial evidence to prioritize diseases and vector surveillance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12936-021-03963-0.

Preparation, characterisation and comparative toxicity of nanopermethrin against Anopheles stephensi and Culex pipiens

OBJECTIVES

To assess the effectiveness of nanopermethrin as a potential new formulation for pest and vector control.

METHODS

Permethrin nanoparticles were prepared by the ionic gelation method and its structure and the formulations were designed using Box-Behnken statistical technique. The effect of independent variables (Chitosan/Permethrin ratio, tripolyphosphate quantity, sonication time) on the properties of nanoparticles was investigated to determine the optimal formulation.

RESULTS

The size of the nanoparticles ranged from 135.27 ± 5.88 to 539.5 ± 24.01 nm and the insecticide entrapment efficiency per cent (EE%) ranged from 7.72 ± 1.36 to 63.59 ± 3.17%. Anopheles stephensi larvae were then bioassayed with the nanopermethrin and compared with the results of the bioassay with the mother molecule of permethrin using a standard WHO-recommended mosquito larval bioassay kit. LC₅₀ with permethrin and nanopermethrin on larvae of An. stephensi were 0.125 and 0.026 ppm showing a 4.8 times difference. The LC₅₀ for permethrin and nanopermethrin on Culex pipiens were 0.003 and 0.00032 ppm, respectively, showing a 9.4-fold difference.

CONCLUSION

Nanopermethrin is much more potent than its mother molecule against larvae of An. stephensi and Cx. pipiens.

Spatial Modelling of Malaria in South of Iran in Line with the Implementation of the Malaria Elimination Program: A Bayesian Poisson-Gamma Random Field Model

Background:

Malaria is the third most important infectious disease in the world. WHO propose programs for controlling and elimination of the disease. Malaria elimination program has begun in first phase in Iran from 2010. Climate factors play an important role in transmission and occurrence of malaria infection. The main goal is to investigate the spatial distribution of incidence of malaria during April 2011 to March 2018 in Hormozgan Province and its association with climate covariates.

Methods:

The data included 882 confirmed cases gathered from CDC in Hormozgan University of Medical Sciences. A Poisson-Gamma Random field model with Bayesian approach was used for modeling the data and produces the smoothed standardized incidence rate (SIR).

Results:

The SIR for malaria ranged from 0 (Abu Musa and Haji Abad districts) to 280.57 (Bandar–e-Jask). Based on model, temperature (RR= 2.29; 95% credible interval: (1.92–2.78)) and humidity (RR= 1.04; 95% credible interval: (1.03–1.06)) had positive effect on malaria incidence, but rainfall (RR= 0.92; 95% credible interval: (0.90–0.95)) had negative impact. Also, smoothed map represent hot spots in the east of the province and in Qeshm Island.

Conclusion:

Based on the analysis of the study results, it was found that the ecological conditions of the region (temperature, humidity and rainfall) and population displacement play an important role in the incidence of malaria. Therefore, the malaria surveillance system should continue to be active in the region, focusing on high-risk areas of malaria.

Restricted genetic heterogeneity of the Plasmodium vivax transmission-blocking vaccine (TBV) candidate Pvs48/45 in a low transmission setting: Implications for the Plasmodium vivax malaria vaccine development

Plasmodium vivax is the most widespread malaria species parasitizing humans outside Africa, with approximately 100 million cases reported per year. Most human cases of P. vivax are asymptomatic with low parasitemia, making active case detection-based elimination programme challenging and less effective. Despite the widespread distribution of P. vivax, no effective vaccines are currently available. Transmission blocking vaccines have recently emerged as potential vaccine candidates to reduce transmission rates to below the essential levels required for the maintenance of the parasite life cycle. Here, we demonstrated that P. vivax was the predominant species found in a malaria-endemic area, although P. vivax/P. falciparum co-infections were also common. Through genomic sequence analysis and neighbor-joining algorithms, we demonstrated limited genetic heterogeneity in the P. vivax transmission-blocking vaccine candidate Pvs48/45 among clinical isolates of P. vivax. Restricted genetic polymorphism occurred at both nucleotide and amino acid levels. The most frequent mutation was A → G at nucleotide position 77 (46.7%), whereas the least frequent was C → T at nucleotide position 1230 (3.3%). The occurrence of single nucleotide polymorphisms (SNPs) distribution at 6/8 positions (75%) led to changes in amino acid sequences in the Pvs48/45 loci, whereas 2/8 (25%) of SNPs resulted in no amino acid sequence variations. Consistently, the nucleotide diversity in the Pvs48/45 locus among the P. vivax population studied was extremely low (π = 0.000525). Changes in amino acid sequences in the Pvs48/45 protein did not result in substantial conformational modifications in the tertiary structures of these proteins. Unveiling the population genetic structure and genetic heterogeneity of vaccine target antigens are necessary for rational design of transmission-blocking antibody vaccines and to monitor the vaccine efficacy in clinical trials in endemic areas for malaria.

Vector-borne diseases in Iran: epidemiology and key challenges

Vector-borne diseases have become a global health concern in recent decades as a result of global warming, globalization, growth in international trade and travel, use of insecticide and drug resistance. This review study addressed the key vector-borne diseases and their current status in Iran to emphasize the requirements for further research on vector-borne diseases. The dispersion patterns of these diseases differ in various regions. Some of them such as Crimean-Congo hemorrhagic fever, and Q fever are distributed all across Iran, whereas some others such as plague, leishmaniasis, tularemia, and malaria are restricted to specific areas. The high prevalence of vectors throughout the country necessitates enhancing the monitoring and surveillance of emerging and reemerging vector-borne diseases and their potential vectors.

Models for Heart Failure Admissions and Admission Rates, 2016 through 2018

BACKGROUND

Approximately 6.5 to 6.9 million individuals in the United States have heart failure, and the disease costs approximately $43.6 billion in 2020. This research provides geographical incidence and cost models of this disease in the U.S. and explanatory models to account for hospitals' number of heart failure DRGs using technical, workload, financial, geographical, and time-related variables.

METHODS

The number of diagnoses is forecast using regression (constrained and unconstrained) and ensemble (random forests, extra trees regressor, gradient boosting, and bagging) techniques at the hospital unit of analysis. Descriptive maps of heart failure diagnostic-related groups (DRGs) depict areas of high incidence. State- and county-level spatial and non-spatial regression models of heart failure admission rates are performed. Expenditure forecasts are estimated.

RESULTS

The incidence of heart failure has increased over time with the highest intensities in the East and center of the country; however, several Northern states have seen large increases since 2016. The best predictive model for the number of diagnoses (hospital unit of analysis) was an extremely randomized tree ensemble (predictive R² = 0.86). The important variables in this model included workload metrics and hospital type. State-level spatial lag models using first-order Queen criteria were best at estimating heart failure admission rates (R² = 0.816). At the county level, OLS was preferred over any GIS model based on Moran's I and resultant R²; however, none of the traditional models performed well (R² = 0.169 for the OLS). Gradient-boosted tree models predicted 36% of the total sum of squares; the most important factors were facility workload, mean cash on hand of the hospitals in the county, and mean equity of those hospitals. Online interactive maps at the state and county levels are provided.

CONCLUSIONS

Heart failure and associated expenditures are increasing. Costs of DRGs in the study increased $61 billion from 2016 through 2018. The increase in the more expensive DRG 291 outpaced others with an associated increase of $92 billion. With the increase in demand and steady-state supply of cardiologists, the costs are likely to balloon over the next decade. Models such as the ones presented here are needed to inform healthcare leaders.

Environmental, climatic and host population risk factors of human cystic echinococcosis in southwest of Iran

BACKGROUND

Cystic echinococcosis (CE), a worldwide zoonotic disease, is affected by various biological and environmental factors. We investigated dog/livestock populations, climatic and environmental factors influencing the distribution of human CE cases in Fars province, southwest Iran.

METHODS

We mapped the addresses of 266 hospitalised CE patients (2004-2014) and studied the effects of different temperature models, mean annual rainfall and humidity, number of frosty days, slope, latitude, land covers, close proximity to nomads travel routes, livestock and dog densities on the occurrence of CE using geographical information systems approach. Data were analyzed by logistic regression.

RESULTS

In the multivariate model predicting CE, living in an urban setting and densities of cattle and dogs were the most important CE predictors, sequentially. Dry (rained) farm, density of camel and sheep, close proximity to nomads travel routes, humidity, and slope also were considered as the determinants of CE distribution, when analyzed independently. Slope had a negative correlation with CE while temperature, frost days and latitude were not associated with CE.

CONCLUSIONS

In our study, an urban setting was the most important risk factor and likely due to a combination of the high density of key life cycle hosts, dogs and livestock, a large human susceptible population and the high number of abattoirs. Farmland and humidity were highly suggestive risk factors and these conditions support the increased survival of Echinococcus granulosus eggs in the soil. These findings support the development of strategies for control of disease. More research is needed test optimal interventions.

Malaria Risk Map Using Spatial Multi-Criteria Decision Analysis along Yunnan Border During the Pre-elimination Period

In moving toward malaria elimination, finer scale malaria risk maps are required to identify hotspots for implementing surveillance–response activities, allocating resources, and preparing health facilities based on the needs and necessities at each specific area. This study aimed to demonstrate the use of multi-criteria decision analysis (MCDA) in conjunction with geographic information systems (GISs) to create a spatial model and risk maps by integrating satellite remote-sensing and malaria surveillance data from 18 counties of Yunnan Province along the China–Myanmar border. The MCDA composite and annual models and risk maps were created from the consensus among the experts who have been working and know situations in the study areas. The experts identified and provided relative factor weights for nine socioeconomic and disease ecology factors as a weighted linear combination model of the following: ([Forest coverage × 0.041] + [Cropland × 0.086] + [Water body × 0.175] + [Elevation × 0.297] + [Human population density × 0.043] + [Imported case × 0.258] + [Distance to road × 0.030] + [Distance to health facility × 0.033] + [Urbanization × 0.036]). The expert-based model had a good prediction capacity with a high area under curve. The study has demonstrated the novel integrated use of spatial MCDA which combines multiple environmental factors in estimating disease risk by using decision rules derived from existing knowledge or hypothesized understanding of the risk factors via diverse quantitative and qualitative criteria using both data-driven and qualitative indicators from the experts. The model and fine MCDA risk map developed in this study could assist in focusing the elimination efforts in the specifically identified locations with high risks.

Remote Sensing and Multi-Criteria Evaluation for Malaria Risk Mapping to Support Indoor Residual Spraying Prioritization in the Central Highlands of Madagascar

The National Malaria Control Program (NMCP) in Madagascar classifies Malagasy districts into two malaria situations: districts in the pre-elimination phase and districts in the control phase. Indoor residual spraying (IRS) is identified as the main intervention means to control malaria in the Central Highlands. However, it involves an important logistical mobilization and thus necessitates prioritization of interventions according to the magnitude of malaria risks. Our objectives were to map the malaria transmission risk and to develop a tool to support the Malagasy Ministry of Public Health (MoH) for selective IRS implementation. For the 2014–2016 period, different sources of remotely sensed data were used to update land cover information and substitute in situ climatic data. Spatial modeling was performed based on multi-criteria evaluation (MCE) to assess malaria risk. Models were mainly based on environment and climate. Three annual malaria risk maps were obtained for 2014, 2015, and 2016. Annual parasite incidence data were used to validate the results. In 2016, the validation of the model using a receiver operating characteristic (ROC) curve showed an accuracy of 0.736; 95% CI [0.669–0.803]. A free plugin for QGIS software was made available for NMCP decision makers to prioritize areas for IRS. An annual update of the model provides the basic information for decision making before each IRS campaign. In Madagascar and beyond, the availability of the free plugin for open-source software facilitates the transfer to the MoH and allows further application to other problems and contexts.

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

This paper discusses on the increasing relevancy of geospatial technologies such as geographic information system (GIS) in the public health domain, particularly for the infectious disease surveillance and modelling strategies. Traditionally, the disease mapping tasks have faced many challenges—(1) authors rarely documented the evidence that were used to create map, (2) before evolution of GIS, many errors aroused in mapping tasks which were expanded extremely at global scales, and (3) there were no fidelity assessment of maps which resulted in inaccurate precision. This study on infectious diseases geo-surveillance is divided into four broad sections with emphasis on handling geographical and temporal issues to help in public health decision-making and planning policies: (1) geospatial mapping of diseases using its spatial and temporal information to understand their behaviour across geography; (2) the citizen’s involvement as volunteers in giving health and disease data to assess the critical situation for disease’s spread and prevention in neighbourhood effect; (3) scientific analysis of health-related behaviour using mathematical epidemiological and geo-statistical approaches with (4) capacity building program. To illustrate each theme, recent case studies are cited and case studies are performed on COVID-19 to demonstrate selected models.

Prediction mapping of human leptospirosis using ANN, GWR, SVM and GLM approaches

Background

Recent reports of the National Ministry of Health and Treatment of Iran (NMHT) show that Gilan has a higher annual incidence rate of leptospirosis than other provinces across the country. Despite several efforts of the government and NMHT to eradicate leptospirosis, it remains a public health problem in this province. Modelling and Prediction of this disease may play an important role in reduction of the prevalence.

Methods

This study aims to model and predict the spatial distribution of leptospirosis utilizing Geographically Weighted Regression (GWR), Generalized Linear Model (GLM), Support Vector Machine (SVM) and Artificial Neural Network (ANN) as capable approaches. Five environmental parameters of precipitation, temperature, humidity, elevation and vegetation are used for modelling and predicting of the disease. Data of 2009 and 2010 are used for training, and 2011 for testing and evaluating the models.

Results

Results indicate that utilized approaches in this study can model and predict leptospirosis with high significance level. To evaluate the efficiency of the approaches, MSE (GWR = 0.050, SVM = 0.137, GLM = 0.118 and ANN = 0.137), MAE (0.012, 0.063, 0.052 and 0.063), MRE (0.011, 0.018, 0.017 and 0.018) and R² (0.85, 0.80, 0.78 and 0.75) are used.

Conclusion

Results indicate the practical usefulness of approaches for spatial modelling and predicting leptospirosis. The efficiency of models is as follow: GWR > SVM > GLM > ANN. In addition, temperature and humidity are investigated as the most influential parameters. Moreover, the suitable habitat of leptospirosis is mostly within the central rural districts of the province.

Species composition, seasonal abundance and distribution of potential anopheline vectors in a malaria endemic area of Iran: field assessment for malaria elimination

Background

Despite decreases in incidence, malaria remains a major public health challenge in south and southeast Iran, where vector control is considered one of the main strategies for elimination of the disease. The efficacy of this strategy depends on understanding malaria vector ecology, which varies by species. This study was conducted to determine the species composition, seasonal abundance and distribution of potential anopheline vectors in Bashagard County, one of the important malaria-endemic areas in south Iran.

Methods

In this cross-sectional study, four typical foothill and mountainous villages were selected by simple random sampling. Anopheline mosquitoes were collected by the standard dipping method for larvae and total catch for adults. Anopheline specimens were morphologically identified using taxonomic keys. Statistical analyses were performed using SPSS ver.20 software.

Results

In total, 1211 anopheline specimens, including 1055 (87.12%) larvae and 156 (12.88%) adults, were collected and identified. They consisted of 9 species: Anopheles moghulensis (27.89%), Anopheles dthali (18.91%), Anopheles culicifacies (16.60%), Anopheles stephensi (15.38%), Anopheles turkhudi (9.83%), Anopheles superpictus (9.66%), Anopheles apoci (1.40%), Anopheles fluviatilis (0.17%), and Anopheles sergentii (0.08%). The most prevalent species in adult catches were An. culicifacies (44.23%), An. dthali (21.15%) and An. stephensi (16.03%), and the most prevalent species caught as larvae were An. moghulensis (31.94%), An. dthali (18.85%) and An. stephensi (15.26%). Most of the anopheline species were distributed in different topographical areas and two proven malaria vectors, An. culicifacies and An. superpictus, were significantly associated with altitude and collected more frequently in the foothill regions. Most of the anopheline species were present almost throughout the year with a major peak in April and a smaller peak in October.

Conclusion

The results of this study revealed that there are five malaria vectors in Bashagard County and some of them are more abundant in areas with special topographic features and are reproductively active throughout the year. These findings can be applied to successful planning and providing effective control measures in problematic areas during the malaria elimination phase in Iran.

Mapping potential Anopheles gambiae s.l. larval distribution using remotely sensed climatic and environmental variables in Baringo, Kenya

Anopheles gambiae s.l. (Diptera: Culicidae) is responsible for the transmission of the devastating Plasmodium falciparum (Haemosporida: Plasmodiidae) strain of malaria in Africa. This study investigated the relationship between climate and environmental conditions and An. gambiae s.l. larvae abundance and modelled the larval distribution of this species in Baringo County, Kenya. Mosquito larvae were collected using a 350-mL dipper and a pipette once per month from December 2015 to December 2016. A random forest algorithm was used to generate vegetation cover classes. A negative binomial regression was used to model the association between remotely sensed climate (rainfall and temperature) and environmental (vegetation cover, vegetation health, topographic wetness and slope) factors and An. gambiae s.l. for December 2015. Anopheles gambiae s.l. was significantly more frequent in the riverine zone (P < 0.05, r = 0.59) compared with the lowland zone. Rainfall (b = 6.22, P < 0.001), slope (b = - 4.81, P = 0.012) and vegetation health (b = - 5.60, P = 0.038) significantly influenced the distribution of An. gambiae s.l. larvae. High An. gambiae s.l. abundance was associated with cropland and wetland environments. Effective malaria control will require zone-specific interventions such as a focused dry season vector control strategy in the riverine zone.

Could Malaria Control Programmes be Timed to Coincide with Onset of Rainfall?

Malaria cases in South Africa's Northern Province of Limpopo have surpassed known endemic KwaZulu Natal and Mpumalanga Provinces. This paper applies statistical methods: regression analysis and impulse response function to understand the timing of impact and the length that such impacts last. Climate data (rainfall and temperature) are obtained from South African Weather Services (SAWs); global data from the European Centre for Medium-Range Weather Forecasts (ECMWF), while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province). Data collected span from January 1998 to July 2007. Signs of the coefficients are positive for rainfall and temperature and negative for their exponents. Three out of five independent variables consistently maintain a very high statistical level of significance. The coefficients for climate variables describe an inverted u-shape: parameters for the exponents of rainfall (-0.02, -0.01, -0.02, -0.00) and temperature (-46.61, -47.46, -48.14, -36.04) are both negative. A one standard deviation rise in rainfall (rainfall onset) increases malaria cases, and the effects become sustained for at least 3 months and conclude that onset of rainfall therefore triggers a 'malaria season'. Malaria control programme and early warning system should be intensified in the first 3 months following the onset of rainfall.

Risk mapping of West Nile virus circulation in Spain, 2015

West Nile fever is an emergent disease in Europe. The objective of this study was to conduct a predictive risk mapping of West Nile Virus (WNV) circulation in Spain based on historical data of WNV circulation. Areas of Spain with evidence of WNV circulation were mapped based on data from notifications to the surveillance systems and a literature review. A logistic regression-based spatial model was used to assess the probability of WNV circulation. Data were analyzed at municipality level. Mean temperatures of the period from June to October, presence of wetlands and presence of Special Protection Areas for birds were considered as potential predictors. Two predictors of WNV circulation were identified: higher temperature [adjusted odds ratio (AOR) 2.07, 95% CI 1.82-2.35, p<0.01] and presence of wetlands (3.37, 95% CI 1.89-5.99, p<0.01). Model validations indicated good predictions: area under the ROC curve was 0.895 (95% CI 0.870-0.919) for internal validation and 0.895 (95% CI 0.840-0.951) for external validation. This model could support improvements of WNV risk- based surveillance in Spain. The importance of a comprehensive surveillance for WNF, including human, animal and potential vectors is highlighted, which could additionally result in model refinements.

Mapping Risk of Malaria Transmission in Mainland Portugal Using a Mathematical Modelling Approach

Malaria is currently one of the world´s major health problems. About a half-million deaths are recorded every year. In Portugal, malaria cases were significantly high until the end of the 1950s but the disease was considered eliminated in 1973. In the past few years, endemic malaria cases have been recorded in some European countries. With the increasing human mobility from countries with endemic malaria to Portugal, there is concern about the resurgence of this disease in the country. Here, we model and map the risk of malaria transmission for mainland Portugal, considering 3 different scenarios of existing imported infections. This risk assessment resulted from entomological studies on An. atroparvus, the only known mosquito capable of transmitting malaria in the study area. We used the malariogenic potential (determined by receptivity, infectivity and vulnerability) applied over geospatial data sets to estimate spatial variation in malaria risk. The results suggest that the risk exists, and the hotspots are concentrated in the northeast region of the country and in the upper and lower Alentejo regions.

Cutaneous leishmaniasis prevalence and morbidity based on environmental factors in Ilam, Iran: Spatial analysis and land use regression models

The aim of this study was to investigate the impact of the environmental factors on cutaneous leishmaniasis (CL) prevalence and morbidity in Ilam province, western Iran, as a known endemic area for this disease. Accurate locations of 3237 CL patients diagnosed from 2013 to 2015, their demographic information, and data of 17 potentially predictive environmental variables (PPEVs) were prepared to be used in Geographic Information System (GIS) and Land-Use Regression (LUR) analysis. The prevalence, risk, and predictive risk maps were provided using Inverse Distance Weighting (IDW) model in GIS software. Regression analysis was used to determine how environmental variables affect on CL prevalence. All maps and regression models were developed based on the annual and three-year average of the CL prevalence. The results showed that there was statistically significant relationship (P value≤0.05) between CL prevalence and 11 (64%) PPEVs which were elevation, population, rainfall, temperature, urban land use, poorland, dry farming, inceptisol and aridisol soils, and forest and irrigated lands. The highest probability of the CL prevalence was predicted in the west of the study area and frontier with Iraq. An inverse relationship was found between CL prevalence and environmental factors, including elevation, covering soil, rainfall, agricultural irrigation, and elevation while this relation was positive for temperature, urban land use, and population density. Environmental factors were found to be an important predictive variables for CL prevalence and should be considered in management strategies for CL control.

Bayesian spatial analysis of the surgical incidence rate of human cystic echinococcosis in north-eastern Iran

BACKGROUND

Cystic echinococcosis (CE) is a zoonotic disease that presents a public health challenge and a socioeconomic burden on developing areas in the Middle East. This study used spatial methods to assess the distribution of surgically managed CE cases in an endemic region of north-eastern Iran.

METHODS

For the years 2001-2007, a case series of all 446 patients that were surgically treated for CE in a referral hospital in north-eastern Iran was evaluated. Patients seen at the referral hospital represent 35 counties in three provinces (Razavi Khorasan, North Khorasan, and South Khorasan). A Besag, York and Mollie (BYM) spatial model was used to produce smoothed standardized incidence ratios (SIRs) for surgically managed cases of CE for the 35 counties represented in this study.

RESULTS

Out of 446 surgically managed patients, 54% were male. County-level crude incidence rates ranged from 0 to 3.27 cases per 100,000 population. The highest smoothed SIR (3.46) was for Sarakhs County in the province of Razavi Khorasan, while the lowest smoothed SIR (0.05) was for Birjand County, located in the province of South Khorasan.

CONCLUSION

SIRs for CE were highest for the province of Razavi Khorasan, which has large ranching and agricultural industries. Additional studies are needed to better evaluate the role of climate, land cover, and livestock rearing on local Echinococcus granulosus transmission in Iran.

Spatial Distribution of Scorpion Sting in a High-Risk Area of Southern Iran

Scorpion sting is a public health problem in south and southwestern parts of Iran, with about 36,000 cases recorded annually. This study aimed to find the spatial distribution of scorpions and their stings in Bandar Abbas County. Monthly scorpion sting cases at the village level were obtained and used for mapping. Scorpions were collected from 14 collection sites using a UV lamp at night and searching under stones during the day time. During the study period, a total of 3,971 cases of scorpion sting were recorded, most of them were found in mountainous areas and affected individuals aged 25-44 yrs. In total, 18 scorpion species belonging to 10 genera were collected and identified. The peak of scorpion sting cases occurred from July to September. The northern part of the mountainous areas had a richer species composition. Hemiscorpius persicus and Hemiscorpius gaillardi were collected for the first time in the area. There were 22 scorpion species in the area across studies; among them, 10 were most dangerous. Hemiscorpius genus is the main etiologic agent in Bandar Abbas County. Mapping dangerous species allows the health system to provide relevant anti-scorpion venom serum accordingly and more cost-effectively.

Crowdsourcing Vector Surveillance: Using Community Knowledge and Experiences to Predict Densities and Distribution of Outdoor-Biting Mosquitoes in Rural Tanzania

Lack of reliable techniques for large-scale monitoring of disease-transmitting mosquitoes is a major public health challenge, especially where advanced geo-information systems are not regularly applicable. We tested an innovative crowd-sourcing approach, which relies simply on knowledge and experiences of residents to rapidly predict areas where disease-transmitting mosquitoes are most abundant. Guided by community-based resource persons, we mapped boundaries and major physical features in three rural Tanzanian villages. We then selected 60 community members, taught them basic map-reading skills, and offered them gridded maps of their own villages (grid size: 200m×200m) so they could identify locations where they believed mosquitoes were most abundant, by ranking the grids from one (highest density) to five (lowest density). The ranks were interpolated in ArcGIS-10 (ESRI-USA) using inverse distance weighting (IDW) method, and re-classified to depict areas people believed had high, medium and low mosquito densities. Finally, we used odor-baited mosquito traps to compare and verify actual outdoor mosquito densities in the same areas. We repeated this process for 12 months, each time with a different group of 60 residents. All entomological surveys depicted similar geographical stratification of mosquito densities in areas classified by community members as having high, medium and low vector abundance. These similarities were observed when all mosquito species were combined, and also when only malaria vectors were considered. Of the 12,412 mosquitoes caught, 60.9% (7,555) were from areas considered by community members as having high mosquito densities, 28% (3,470) from medium density areas, and 11.2% (1,387) from low density areas. This study provides evidence that we can rely on community knowledge and experiences to identify areas where mosquitoes are most abundant or least abundant, even without entomological surveys. This crowd-sourcing method could be further refined and validated to improve community-based planning of mosquito control operations at low-cost.

Zoonotic cutaneous leishmaniasis in northeastern Iran: a GIS-based spatio-temporal multi-criteria decision-making approach

Zoonotic cutaneous leishmaniasis (ZCL) constitutes a serious public health problem in many parts of the world including Iran. This study was carried out to assess the risk of the disease in an endemic province by developing spatial environmentally based models in yearly intervals. To fill the gap of underestimated true burden of ZCL and short study period, analytical hierarchy process (AHP) and fuzzy AHP decision-making methods were used to determine the ZCL risk zones in a Geographic Information System platform. Generated risk maps showed that high-risk areas were predominantly located at the northern and northeastern parts in each of the three study years. Comparison of the generated risk maps with geocoded ZCL cases at the village level demonstrated that in both methods more than 90%, 70% and 80% of the cases occurred in high and very high risk areas for the years 2010, 2011, and 2012, respectively. Moreover, comparison of the risk categories with spatially averaged normalized difference vegetation index (NDVI) images and a digital elevation model of the study region indicated persistent strong negative relationships between these environmental variables and ZCL risk degrees. These findings identified more susceptible areas of ZCL and will help the monitoring of this zoonosis to be more targeted.

Spatial distribution of individuals with symptoms of depression in a periurban area in Lima: an example from Peru

PURPOSE

To map the geographical distribution and spatial clustering of depressive symptoms cases in an area of Lima, Peru.

METHODS

Presence of depressive symptoms suggesting a major depressive episode was assessed using a short version of the Center for Epidemiologic Studies Depression Scale. Data were obtained from a census conducted in 2010. One participant per selected household (aged 18 years and above, living more than 6 months in the area) was included. Residence latitude, longitude, and elevation were captured using a GPS device. The prevalence of depressive symptoms was estimated, and relative risks (RRs) were calculated to identify areas of significantly higher and lower geographical concentrations of depressive symptoms.

RESULTS

Data from 7946 participants, 28.3% male, mean age 39.4 (SD, 13.9) years, were analyzed. The prevalence of depressive symptoms was 17.0% (95% confidence interval = 16.2%-17.8%). Three clusters with high prevalence of depressive symptoms (primary cluster: RR = 1.82; P = .003 and secondary: RR = 2.83; P = .004 and RR = 5.92; P = .01), and two clusters with significantly low prevalence (primary: RR = 0.23; P = .016 and secondary: RR = 0; P = .035), were identified. Further adjustment by potential confounders confirmed the high prevalence clusters but also identified newer ones.

CONCLUSIONS

Screening strategies for depression, in combination with mapping techniques, may be useful tools to target interventions in resource-limited areas.

Socio-economic, epidemiological and geographic features based on GIS-integrated mapping to identify malarial hotspots

Background

Malaria is a major health problem in the tropical and subtropical world. In India, 95% of the population resides in malaria endemic regions and it is major public health problem in most parts of the country. The present work has developed malaria maps by integrating socio-economic, epidemiology and geographical dimensions of three eastern districts of Uttar Pradesh, India. The area has been studied in each dimension separately, and later integrated to find a list of vulnerable pockets/villages, called as malarial hotspots.

Methods

The study has been done at village level. Seasonal variation of malaria, comparison of epidemiology indices and progress of the medical facility were studied. Ten independent geographical information system (GIS) maps of socio-economic aspects (population, child population, literacy, and work force participation), epidemiology (annual parasitic index (API) and slides collected and examined) and geographical features (settlement, forest cover, water bodies, rainfall, relative humidity, and temperature) were drawn and studied. These maps were overlaid based on computed weight matrix to find malarial hotspot.

Results

It was found that the studied dimensions were inter-weaving factors for malaria epidemic and closely affected malaria situations as evidenced from the obtained correlation matrix. The regions with water logging, high rainfall and proximity to forest, along with poor socio-economic conditions, are primarily hotspot regions. The work is presented through a series of GIS maps, tables, figures and graphs. A total of 2,054 out of 8,973 villages studied were found to be malarial hotspots and consequently suggestions were made to the concerned government malaria offices.

Conclusion

With developing technology, information tools such as GIS, have captured almost every field of scientific research especially of vector-borne diseases, such as malaria. Malarial mapping enables easy update of information and effortless accessibility of geo-referenced data to policy makers to produce cost-effective measures for malaria control in endemic regions.

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-015-0685-4) contains supplementary material, which is available to authorized users.

Spatial modeling of colonic lesions with geographic information systems

BACKGROUND

Geographic information system (GIS) software has been used in health care systems to display and analyze spatial pattern of diseases and health services.

OBJECTIVES

This study was performed to assess spatial patterns of colon's pathologic lesions based on the pathologic reports and assess whether it is possible to use GIS software in health services.

PATIENTS AND METHODS

Archives of pathology of Namazi and Faghihi hospitals, two main referral centers of south-west of Iran, were obtained and reviewed between January 2009 and September 2011 for biopsy reports of patients who underwent colonoscopy. Abnormal biopsies were categorized into five different subgroups according to the type of pathologic specimens. By GIS, spatial patterns of colon biopsies were plotted in different maps and spatial auto-correlation of colon biopsies was calculated using the Moran's Index.

RESULTS

A total of 4815 biopsies from 2663 different patients were reviewed, 53.8% of which were men. Abnormal biopsies were 2781 of all specimens (57.8%). Neoplastic lesions, inflammatory bowel diseases and polyps were 9.3%, 19.3% and 29.2% of total biopsies, respectively. Pathologic biopsies were more common in the distal colon. Maps of all biopsies and maps of specific pathologies were manifested in GIS.

CONCLUSIONS

Our study showed that left-sided lesions are still more common in the Iranian population. On the other hand, surveying the right side of colon is as important as the distal part, which necessitates total colonoscopy.

Long-run relative importance of temperature as the main driver to malaria transmission in Limpopo Province, South Africa: a simple econometric approach

Malaria in Limpopo Province of South Africa is shifting and now observed in originally non-malaria districts, and it is unclear whether climate change drives this shift. This study examines the distribution of malaria at district level in the province, determines direction and strength of the linear relationship and causality between malaria with the meteorological variables (rainfall and temperature) and ascertains their short- and long-run variations. Spatio-temporal method, Correlation analysis and econometric methods are applied. Time series monthly meteorological data (1998-2007) were obtained from South Africa Weather Services, while clinical malaria data came from Malaria Control Centre in Tzaneen (Limpopo Province) and South African Department of Health. We find that malaria changes and pressures vary in different districts with a strong positive correlation between temperature with malaria, r = 0.5212, and a weak positive relationship for rainfall, r = 0.2810. Strong unidirectional causality runs from rainfall and temperature to malaria cases (and not vice versa): F (1, 117) = 3.89, ρ = 0.0232 and F (1, 117) = 20.08, P < 0.001 and between rainfall and temperature, a bi-directional causality exists: F (1, 117) = 19.80; F (1,117) = 17.14, P < 0.001, respectively, meaning that rainfall affects temperature and vice versa. Results show evidence of strong existence of a long-run relationship between climate variables and malaria, with temperature maintaining very high level of significance than rainfall. Temperature, therefore, is more important in influencing malaria transmission in Limpopo Province.

Mapping malaria risk and vulnerability in the United Republic of Tanzania: a spatial explicit model

Background

Outbreaks of vector-borne diseases (VBDs) impose a heavy burden on vulnerable populations. Despite recent progress in eradication and control, malaria remains the most prevalent VBD. Integrative approaches that take into account environmental, socioeconomic, demographic, biological, cultural, and political factors contributing to malaria risk and vulnerability are needed to effectively reduce malaria burden. Although the focus on malaria risk has increasingly gained ground, little emphasis has been given to develop quantitative methods for assessing malaria risk including malaria vulnerability in a spatial explicit manner.

Methods

Building on a conceptual risk and vulnerability framework, we propose a spatial explicit approach for modeling relative levels of malaria risk - as a function of hazard, exposure, and vulnerability - in the United Republic of Tanzania. A logistic regression model was employed to identify a final set of risk factors and their contribution to malaria endemicity based on multidisciplinary geospatial information. We utilized a Geographic Information System for the construction and visualization of a malaria vulnerability index and its integration into a spatially explicit malaria risk map.

Results

The spatial pattern of malaria risk was very heterogeneous across the country. Malaria risk was higher in Mainland areas than in Zanzibar, which is a result of differences in both malaria entomological inoculation rate and prevailing vulnerabilities. Areas of high malaria risk were identified in the southeastern part of the country, as well as in two distinct “hotspots” in the northwestern part of the country bordering Lake Victoria, while concentrations of high malaria vulnerability seem to occur in the northwestern, western, and southeastern parts of the mainland. Results were visualized using both 10×10 km² grids and subnational administrative units.

Conclusions

The presented approach makes an important contribution toward a decision support tool. By decomposing malaria risk into its components, the approach offers evidence on which factors could be targeted for reducing malaria risk and vulnerability to the disease. Ultimately, results offer relevant information for place-based intervention planning and more effective spatial allocation of resources.

Electronic supplementary material

The online version of this article (doi:10.1186/s12963-015-0036-2) contains supplementary material, which is available to authorized users.

Spatiotemporal approaches to analyzing pedestrian fatalities: the case of Cali, Colombia

OBJECTIVE

Injuries among pedestrians are a major public health concern in Colombian cities such as Cali. This is one of the first studies in Latin America to apply Bayesian maximum entropy (BME) methods to visualize and produce fine-scale, highly accurate estimates of citywide pedestrian fatalities. The purpose of this study is to determine the BME method that best estimates pedestrian mortality rates and reduces statistical noise. We further utilized BME methods to identify and differentiate spatial patterns and persistent versus transient pedestrian mortality hotspots.

METHODS

In this multiyear study, geocoded pedestrian mortality data from the Cali Injury Surveillance System (2008 to 2010) and census data were utilized to accurately visualize and estimate pedestrian fatalities. We investigated the effects of temporal and spatial scales, addressing issues arising from the rarity of pedestrian fatality events using 3 BME methods (simple kriging, Poisson kriging, and uniform model Bayesian maximum entropy). To reduce statistical noise while retaining a fine spatial and temporal scale, data were aggregated over 9-month incidence periods and censal sectors. Based on a cross-validation of BME methods, Poisson kriging was selected as the best BME method. Finally, the spatiotemporal and urban built environment characteristics of Cali pedestrian mortality hotspots were linked to intervention measures provided in Mead et al.'s (2014) pedestrian mortality review.

RESULTS

The BME space-time analysis in Cali resulted in maps displaying hotspots of high pedestrian fatalities extending over small areas with radii of 0.25 to 1.1 km and temporal durations of 1 month to 3 years. Mapping the spatiotemporal distribution of pedestrian mortality rates identified high-priority areas for prevention strategies. The BME results allow us to identify possible intervention strategies according to the persistence and built environment of the hotspot; for example, through enforcement or long-term environmental modifications.

CONCLUSIONS

BME methods provide useful information on the time and place of injuries and can inform policy strategies by isolating priority areas for interventions, contributing to intervention evaluation, and helping to generate hypotheses and identify the preventative strategies that may be suitable to those areas (e.g., street-level methods: pedestrian crossings, enforcement interventions; or citywide approaches: limiting vehicle speeds). This specific information is highly relevant for public health interventions because it provides the ability to target precise locations.

Identification of Suitable Areas for African Horse Sickness Virus Infections in Spanish Equine Populations

African horse sickness (AHS) is one of the most important vector-borne viral infectious diseases of equines, transmitted mainly by Culicoides spp. The re-emergence of Culicoides-borne diseases in Europe, such as the recent bluetongue (BT) or Schmallenberg outbreaks, has raised concern about the potential re-introduction and further spread of AHS virus (AHSV) in Europe. Spain has one of the largest European equine populations. In addition, its geographical, environmental and entomological conditions favour AHSV infections, as shown by the historical outbreaks in the 1990s. The establishment of risk-based surveillance strategies would allow the early detection and rapid control of any potential AHSV outbreak. This study aimed to identify the areas and time periods that are suitable or at high risk for AHS occurrence in Spain using a GIS-based multicriteria decision framework. Specifically risk maps for AHS occurrence were produced using a weighted linear combination of the main risk factors of disease, namely extrinsic incubation period, equine density and distribution of competent Culicoides populations. Model results revealed that the south-western and north-central areas of Spain and the Balearic Islands are the areas at the highest risk for AHSV infections, particularly in late summer months. Conversely, Galicia, Castile and Leon and La Rioja can be considered as low-risk regions. This result was validated with historical AHS and BT outbreaks in Spain, and with the Culicoides vector distribution area. The model results, together with current Spanish equine production features, should provide the foundations to design risk-based and more cost-effective surveillance strategies for the early detection and rapid control potential of AHS outbreaks in Spain.

Identification of suitable areas for the occurrence of Rift Valley fever outbreaks in Spain using a multiple criteria decision framework

Rift Valley fever (RVF) is a mosquito-borne viral disease that may produce a considerable impact on the economy in affected countries. In the last decades, the geographic distribution of RVF virus has increased including most of the countries in Africa, Arabia Saudi and Yemen. This situation has raised the concerns regarding its potential introduction in the European Union (EU) countries where the high number of susceptible species and competent vectors may contribute to the spread of the disease and challenge its rapid control. Thus, the identification of the areas and time periods with highest suitability for RVF outbreak occurrence would be useful for improving the early detection and rapid response of the disease into free countries. The objective of this study was to identify suitable areas for the occurrence of RVF outbreaks in Spain using a multiple criteria decision making model based on weighted linear combination of factors in geographical information systems (GIS). To the best of the author's knowledge this is the first comprehensive GIS-based framework that provides risk maps for RVF suitability in an EU country. Spanish zones with the highest suitability for RVF were concentrated in the regions of Extremadura, south-western Castile and Leon, eastern Galicia, Asturias, Cantabria, Basque Country, northern-central and southern region of Andalusia and in the Balearic Islands. October and May were the most suitable months for RVF outbreak occurrence. Methods and results presented here may be useful to target risk-based surveillance strategies and to more cost-effectively control potential RVFV incursions into Spain.