Association of Climatic Variability, Vector Population and Malarial Disease in District of Visakhapatnam, India: A Modeling and Prediction Analysis

Potential impact of climatic factors on malaria in Rwanda between 2012 and 2021: a time-series analysis

BACKGROUND

Malaria remains an important public health problem, particularly in sub-Saharan Africa. In Rwanda, where malaria ranks among the leading causes of mortality and morbidity, disease transmission is influenced by climatic factors. However, there is a paucity of studies investigating the link between climate change and malaria dynamics, which hinders the development of effective national malaria response strategies. Addressing this critical gap, this study analyses how climatic factors influence malaria transmission across Rwanda, thereby informing tailored interventions and enhancing disease management frameworks.

METHODS

The study analysed the potential impact of temperature and cumulative rainfall on malaria incidence in Rwanda from 2012 to 2021 using meteorological data from the Rwanda Meteorological Agency and malaria case records from the Rwanda Health Management and Information System. The analysis was performed in two stages. First, district-specific generalized linear models with a quasi-Poisson distribution were applied, which were enhanced by distributed lag non-linear models to explore non-linear and lagged effects. Second, random effects multivariate meta-analysis was employed to pool the estimates and to refine them through best linear unbiased predictions.

RESULTS

A 1-month lag with specific temperature and rainfall thresholds influenced malaria incidence across Rwanda. Average temperature of 18.5 °C was associated with higher malaria risk, while temperature above 23.9 °C reduced the risk. Rainfall demonstrated a dual effect on malaria risk: conditions of low (below 73 mm per month) and high (above 223 mm per month) precipitation correlated with lower risk, while moderate rainfall (87 to 223 mm per month) correlated with higher risk. Seasonal patterns showed increased malaria risk during the major rainy season, while the short dry season presented lower risk.

CONCLUSION

The study underscores the influence of temperature and rainfall on malaria transmission in Rwanda and calls for tailored interventions that are specific to location and season. The findings are crucial for informing policy that enhance preparedness and contribute to malaria elimination efforts. Future research should explore additional ecological and socioeconomic factors and their differential contribution to malaria transmission.

Determining suitable machine learning classifier technique for prediction of malaria incidents attributed to climate of Odisha

This study investigated the influence of climate factors on malaria incidence in the Sundargarh district, Odisha, India. The WEKA machine learning tool was used with two classifier techniques, Multi-Layer Perceptron (MLP) and J48, with three test options, 10-fold cross-validation, percentile split, and supplied test. A comparative analysis was carried out to ascertain the superior model among malaria prediction accuracy techniques in varying climate contexts. The results suggested that J48 had exhibited better skill than MLP with the 10-fold cross-validation method over the percentile split and supplied test options. J48 demonstrated less error (RMSE = 0.6), better kappa = 0.63, and higher accuracy = 0.71), suggesting it as most suitable model. Seasonal variation of temperature and humidity had a better association with malaria incidents than rainfall, and the performance was better during the monsoon and post-monsoon when the incidents are at the peak.

Spatiotemporal analysis of environmental and physiographic factors related to malaria in Bareilly district, India

Objectives

The aim of this study was to explore the spatiotemporal clustering of reported malaria cases and to study the effects of various environmental and physiographic factors on malaria incidence in Bareilly district, Uttar Pradesh, India.

Methods

Malaria surveillance data were collected from the state health department and cleaned into an analyzable format. These data were analyzed along with meteorological, physiographic, and 2019 population data, which were obtained from the Indian Meteorological Department, National Aeronautics and Space Administration web portal, the Bhuvan platform of the Indian Space Research Organization, and the 2011 Census of India.

Results

In total, 46,717 malaria cases were reported in Bareilly district in 2019, of which 25.99% were Plasmodium vivax cases and 74.01% were P. falciparum cases. The reported malaria cases in the district showed clustering, with significant spatial autocorrelation (Moran’s I value=0.63), and space-time clustering (p<0.01). A significant positive correlation was found between monthly malaria incidence and the monthly mean temperature (with a lag of 1−2 months) and rainfall (with a lag of 1 month). A significant negative correlation was detected between the elevation of blocks (i.e., intermediate-level administrative districts) and annual malaria reporting.

Conclusion

The presence of space-time clustering of malaria cases and its correlation with meteorological and physiographic factors indicate that routine spatial analysis of the surveillance data could help control and manage malaria outbreaks in the district.

Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

Background

In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases.

Methods

In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand.

Results

From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence.

Conclusions

A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

Climate based malaria forecasting system for Andhra Pradesh, India

Malaria is a major public health problem in tropical and subtropical countries of the World. During the year 1999, Visakhapatnam district of Andhra Pradesh, India experienced a major epidemic of malaria, and nearly 41,805 cases were reported. Hence, a retrospective malaria surveillance study was conducted from 2001 to 2016 and reported nearly a total of 149,317 malaria cases during the study period. Of which, Plasmodium vivax contributes 32%, and Plasmodium falciparum contributes 68% of the total cases. Malaria cases follow a strong seasonal variation and 70% of cases are reported during the monsoon periods. In the present study, we exploited multi step polynomial regression and seasonal autoregressive integrated moving average (SARIMA) models to forecast the malaria cases in the study area. The polynomial model predicted malaria cases with high predictive power and found that malaria cases at lag one, and population played a vital role in malaria transmission. Similarly, mean temperature, rainfall and Normalized Difference Vegetation Index build a significant impact on malaria cases. The best fit model was SARIMA (1, 1, 2) (2, 1, 1)12 which was used for forecasting monthly malaria incidence for the period of January 2015 to December 2016. The performance accuracy of both models are similar, however lowest Akaike information criterion score was observed by the polynomial model, and this approach can be helpful further for forecasting malaria incidence to implement effective control measures in advance for combating malaria in India.

Epidemiology of infectious diarrhoea and the relationship with etiological and meteorological factors in Jiangsu Province, China

We depicted the epidemiological characteristics of infectious diarrhoea in Jiangsu Province, China. Generalized additive models were employed to evaluate the age-specific effects of etiological and meteorological factors on prevalence. A long-term increasing prevalence with strong seasonality was observed. In those aged 0–5 years, disease risk increased rapidly with the positive rate of virus (rotavirus, norovirus, sapovirus, astrovirus) in the 20–50% range. In those aged > 20 years, disease risk increased with the positive rate of adenovirus and bacteria (Vibrio parahaemolyticus, Salmonella, Escherichia coli, Campylobacter jejuni) until reaching 5%, and thereafter stayed stable. The mean temperature, relative humidity, temperature range, and rainfall were all related to two-month lag morbidity in the group aged 0–5 years. Disease risk increased with relative humidity between 67–78%. Synchronous climate affected the incidence in those aged >20 years. Mean temperature and rainfall showed U-shape associations with disease risk (with threshold 15 °C and 100 mm per month, respectively). Meanwhile, disease risk increased gradually with sunshine duration over 150 hours per month. However, no associations were found in the group aged 6–19 years. In brief, etiological and meteorological factors had age-specific effects on the prevalence of infectious diarrhoea in Jiangsu. Surveillance efforts are needed to prevent its spread.

Achieving explanatory depth and spatial breadth in infectious disease modelling: Integrating active and passive case surveillance

Ideally, the data used for robust spatial prediction of disease distribution should be both high-resolution and spatially expansive. However, such in-depth and geographically broad data are rarely available in practice. Instead, researchers usually acquire either detailed epidemiological data with high resolution at a small number of active sampling sites, or more broad-ranging but less precise data from passive case surveillance. We propose a novel inferential framework, capable of simultaneously drawing insights from both passive and active data types. We developed a Bayesian latent point process approach, combining active data collection in a limited set of points, where in-depth covariates are measured, with passive case detection, where error-prone, large-scale disease data are accompanied only by coarse or remotely-sensed covariate layers. Using the example of malaria, we tested our method's efficiency under several hypothetical scenarios of reported incidence in different combinations of imperfect detection and spatial complexity of the environmental variables. We provide a simple solution to a widespread problem in spatial epidemiology, combining latent process modelling and spatially autoregressive modelling. By using active sampling and passive case detection in a complementary way, we achieved the best-of-both-worlds, in effect, a formal calibration of spatially extensive, error-prone data by localised, high-quality data.

Epidemiological Characteristics of Re-emerging Vivax Malaria in the Republic of Korea (1993-2017)

Historically, Plasmodium vivax malaria has been one of the most highly endemic parasitic diseases in the Korean Peninsula. Until the 1970s, vivax malaria was rarely directly lethal and was controlled through the Korean Government Program administered by the National Malaria Eradication Service in association with the World Health Organization's Global Malaria Eradication Program. Vivax malaria has re-emerged in 1993 near the Demilitarized Zone between South and North Korea and has since become an endemic infectious disease that now poses a serious public health threat through local transmission in the Republic of Korea. This review presents major lessons learned from past and current malaria research, including epidemiological and biological characteristics of the re-emergent disease, and considers some interesting patterns of diversity. Among other features, this review highlights temporal changes in the genetic make-up of the parasitic population, patient demographic features, and spatial distribution of cases, which all provide insight into the factors contributing to local transmission. The data indicate that vivax malaria in Korea is not expanding expo- nentially. However, continued surveillance is needed to prevent future resurgence.

Seasonal variations in Plasmodium falciparum genetic diversity and multiplicity of infection in asymptomatic children living in southern Ghana

Background

Genetic diversity in Plasmodium falciparum (P. falciparum) parasites is a major hurdle to the control of malaria. This study monitored changes in the genetic diversity and the multiplicity of P. falciparum parasite infection in asymptomatic children living in southern Ghana at 3 month intervals between April 2015 and January 2016.

Methods

Filter paper blood spots (DBS) were collected quarterly from children living in Obom, a community with perennial malaria transmission and Abura, a community with seasonal malaria transmission. Genomic DNA was extracted from the DBS and used in polymerase chain reaction (PCR)-based genotyping of the merozoite surface protein 1 (msp 1) and merozoite surface protein 2 (msp 2) genes.

Results

Out of a total of 787 samples that were collected from the two study sites, 59.2% (466/787) tested positive for P. falciparum. The msp 1 and msp 2 genes were successfully amplified from 73.8% (344/466) and 82.5% (385/466) of the P. falciparum positive samples respectively. The geometric mean MOI in Abura ranged between 1.17 (95% CI: 1.08–1.28) and 1.48 (95% CI: 1.36–1.60) and was significantly lower (p < 0.01, Dunn’s multiple comparison test) than that determined in Obom, where the geometric mean MOI ranged between 1.82 (95% CI: 1.58–2.08) and 2.50 (95% CI: 2.33–2.678) over the study period. Whilst the msp 1 R033:MAD20:KI allelic family ratio was dynamic, the msp 2 3D7:FC27 allelic family ratio remained relatively stable across the changing seasons in both sites.

Conclusions

This study shows that seasonal variations in parasite diversity in these communities can be better estimated by msp 1 rather than msp 2 due to the constantly changing relative intra allelic frequencies observed in msp 1 and the fact that the dominance of any msp 2 allele was dependent on the transmission setting but not on the season as opposed to the dominance of any msp 1 allele, which was dependent on both the season and the transmission setting.

Electronic supplementary material

The online version of this article (10.1186/s12879-018-3350-z) contains supplementary material, which is available to authorized users.

Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990-2011

Malaria remains a significant public health concern in developing countries. Drivers of malaria transmission vary across different geographical regions. Climatic variables are major risk factor in seasonal and secular patterns of P. vivax malaria transmission along Anhui province. The study aims to forecast malaria outbreaks using empirical model developed in Hefei, China. Data on the monthly numbers of notified malaria cases and climatic factors were obtained for the period of January 1st 1990 to December 31st 2011 from the Hefei CDC and Anhui Institute of Meteorological Sciences, respectively. Two logistic regression models with time series seasonal decomposition were used to explore the impact of climatic and seasonal factors on malaria outbreaks. Sensitivity and specificity statistics were used for evaluating the predictive power. The results showed that relative humidity (OR = 1.171, 95% CI = 1.090-1.257), sunshine (OR = 1.076, 95% CI = 1.043-1.110) and barometric pressure (OR = 1.051, 95% CI = 1.003-1.100) were significantly associated with malaria outbreaks after adjustment for seasonality in Hefei area. The validation analyses indicated the overall agreement of 70.42% (sensitivity: 70.52%; specificity: 70.30%). The research suggested that the empirical model developed based on disease surveillance and climatic conditions may have applications in malaria control and prevention activities.

Seasonal variation of tsetse fly species abundance and prevalence of trypanosomes in the Maasai Steppe, Tanzania

Tsetse flies, the vectors of trypanosomiasis, represent a threat to public health and economy in sub-Saharan Africa. Despite these concerns, information on temporal and spatial dynamics of tsetse and trypanosomes remain limited and may be a reason that control strategies are less effective. The current study assessed the temporal variation of the relative abundance of tsetse fly species and trypanosome prevalence in relation to climate in the Maasai Steppe of Tanzania in 2014-2015. Tsetse flies were captured using odor-baited Epsilon traps deployed in ten sites selected through random subsampling of the major vegetation types in the area. Fly species were identified morphologically and trypanosome species classified using PCR. The climate dataset was acquired from the African Flood and Drought Monitor repository. Three species of tsetse flies were identified: G. swynnertoni (70.8%), G. m. morsitans (23.4%), and G.pallidipes (5.8%). All species showed monthly changes in abundance with most of the flies collected in July. The relative abundance of G. m. morsitans and G. swynnertoni was negatively correlated with maximum and minimum temperature, respectively. Three trypanosome species were recorded: T. vivax (82.1%), T. brucei (8.93%), and T. congolense (3.57%). The peak of trypanosome infections in the flies was found in October and was three months after the tsetse abundance peak; prevalence was negatively correlated with tsetse abundance. A strong positive relationship was found between trypanosome prevalence and temperature. In conclusion, we find that trypanosome prevalence is dependent on fly availability, and temperature drives both tsetse fly relative abundance and trypanosome prevalence.

Modelling the influence of climate on malaria occurrence in Chimoio Municipality, Mozambique

Background

Mozambique was recently ranked fifth in the African continent for the number of cases of malaria. In Chimoio municipality cases of malaria are increasing annually, contrary to the decreasing trend in Africa. As malaria transmission is influenced to a large extent by climatic conditions, modelling this relationship can provide useful insights for designing precision health measures for malaria control. There is a scarcity of information on the association between climatic variability and malaria transmission risk in Mozambique in general, and in Chimoio in particular. Therefore, the aim of this study is to model the association between climatic variables and malaria cases on a weekly basis, to help policy makers find adequate measures for malaria control and eradication.

Methods

Time series analysis was conducted using data on weekly climatic variables and weekly malaria cases (counts) in Chimoio municipality, from 2006 to 2014. All data were analysed using SPSS-20, R 3.3.2 and BioEstat 5.0. Cross-correlation analysis, linear processes, namely ARIMA models and regression modelling, were used to develop the final model.

Results

Between 2006 and 2014, 490,561 cases of malaria were recorded in Chimoio. Both malaria and climatic data exhibit weekly and yearly systematic fluctuations. Cross-correlation analysis showed that mean temperature and precipitation present significantly lagged correlations with malaria cases. An ARIMA model (2,1,0) (2,1,1)₅₂, and a regression model for a Box-Cox transformed number of malaria cases with lags 1, 2 and 3 of weekly malaria cases and lags 6 and 7 of weekly mean temperature and lags 12 of precipitation were fitted. Although, both produced similar widths for prediction intervals, the last was able to anticipate malaria outbreak more accurately.

Conclusion

The Chimoio climate seems ideal for malaria occurrence. Malaria occurrence peaks during January to March in Chimoio. As the lag effect between climatic events and malaria occurrence is important for the prediction of malaria cases, this can be used for designing public precision health measures. The model can be used for planning specific measures for Chimoio municipality. Prospective and multidisciplinary research involving researchers from different fields is welcomed to improve the effect of climatic factors and other factors in malaria cases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13071-017-2205-6) contains supplementary material, which is available to authorized users.

Analysing malaria incidence at the small area level for developing a spatial decision support system: A case study in Kalaburagi, Karnataka, India

Spatial decision support systems have already proved their value in helping to reduce infectious diseases but to be effective they need to be designed to reflect local circumstances and local data availability. We report the first stage of a project to develop a spatial decision support system for infectious diseases for Karnataka State in India. The focus of this paper is on malaria incidence and we draw on small area data on new cases of malaria analysed in two-monthly time intervals over the period February 2012 to January 2016 for Kalaburagi taluk, a small area in Karnataka. We report the results of data mapping and cluster detection (identifying areas of excess risk) including evaluating the temporal persistence of excess risk and the local conditions with which high counts are statistically associated. We comment on how this work might feed into a practical spatial decision support system.

The effects of electricity network development besides routine malaria control measures in an underdeveloped region in the pre-elimination phase

Background

The main purpose of this study was to investigate the effects of electricity network development on malaria transmission. The study was performed in the rural areas of three districts in Sistan-va-Baluchestan Province, Iran.

Methods

From the mentioned districts, 122 rural communities were selected. The data of the years 2005–2009 were collected retrospectively from data banks of the district health centres and the offices of the local electricity network. Fixed and random effects panel data regression models were fitted to determine the effects of electrification and other variables on malaria transmission during the elimination phase.

Results

It seems that access to electricity of rural communities, if not harmful, has no obvious effect on malaria control and prevention at least during the elimination phase in an underdeveloped region. Elevation above sea level and precipitation during spring and summer were found to be the other important, respectively, time-invariant and time-dependent variables associated with decreasing and increasing malaria transmission. Indoor residual spraying and the use of insecticide-treated mosquito nets were not found to be effective in decreasing malaria transmission in the elimination phase.

Conclusions

The introduction of electricity to a rural community does not guarantee an absolutely good effect on the reduction of malaria transmission.

Estimating malaria parasite prevalence from community surveys in Uganda: a comparison of microscopy, rapid diagnostic tests and polymerase chain reaction

Background

Household surveys are important tools for monitoring the malaria disease burden and measuring impact of malaria control interventions with parasite prevalence as the primary metric. However, estimates of parasite prevalence are dependent on a number of factors including the method used to detect parasites, age of the population sampled, and level of immunity. To better understand the influence of diagnostics, age, and endemicity on estimates of parasite prevalence and how these change over time, community-based surveys were performed for two consecutive years in three settings and the sensitivities of microscopy and immunochromatographic rapid diagnostic tests (RDTs) were assessed, considering polymerase chain reaction (PCR) as the gold standard.

Methods

Surveys were conducted over the same two-month period in 2012 and 2013 in each of three sub-counties in Uganda: Nagongera in Tororo District (January–February), Walukuba in Jinja District (March–April), and Kihihi in Kanungu District (May–June). In each sub-county, 200 households were randomly enrolled and a household questionnaire capturing information on demographics, use of malaria prevention methods, and proxy indicators of wealth was administered to the head of the household. Finger-prick blood samples were obtained for RDTs, measurement of hemoglobin, thick and thin blood smears, and to store samples on filter paper.

Results

A total of 1200 households were surveyed and 4433 participants were included in the analysis. Compared to PCR, the sensitivity of microscopy was low (65.3 % in Nagongera, 49.6 % in Walukuba and 40.9 % in Kihihi) and decreased with increasing age. The specificity of microscopy was over 98 % at all sites and did not vary with age or year. Relative differences in parasite prevalence across different age groups, study sites, and years were similar for microscopy and PCR. The sensitivity of RDTs was similar across the three sites (range 77.2–82.8 %), was consistently higher than microscopy (p < 0.001 for all pairwise comparisons), and decreased with increasing age. The specificity of RDTs was lower than microscopy (76.3 % in Nagongera, 86.3 % in Walukuba, and 83.5 % in Kihihi) and varied significantly by year and age. Relative differences in parasite prevalence across age groups and study years differed for RDTs compared to microscopy and PCR.

Conclusion

Malaria prevalence estimates varied with diagnostic test, age, and transmission intensity. It is important to consider the effects of these parameters when designing and interpreting community-based surveys.

Population dynamics and community structure of Anopheles mosquitoes along the China-Myanmar border

BACKGROUND

Understanding the ecology of malaria vectors such as species composition and population dynamics is essential for developing cost-effective strategies to control mosquito vector populations.

METHODS

Adult mosquitoes (n = 79,567) were collected in five villages along the China-Myanmar border from April 2012 to September 2014 using the CDC light trap without bait method. Mosquito community structure, Anopheles species composition and diversity were analyzed.

RESULTS

Twenty species of Anopheles mosquitoes were identified, with An. minimus s.l. accounting for 85% of the total collections. Mosquito densities varied from 0.05 females per trap per night (f/t/n) to 3.00 f/t/n, with strong seasonality in all sites and densities peaked from June to August. An. minimus s.l. was predominant (accounting for 54-91% of total captures) in four villages, An. maculatus s.l. was predominant (71%) in the high elevation village of Dao Nong, and An. culicifacies accounted for 15% of total captures in the peri-urban area of Simsa Lawk. All 20 species have been captured in the Mung Seng Yang village, 18 and 15 species in Ja Htu Kawng and Na Bang respectively, and nine species in both Simsa Lawk and Dao Nong. Species richness peaked from April to August. Species diversity, species dominance index, and species evenness fluctuated substantially from time to time with no clear seasonality, and varied greatly amongst villages.

CONCLUSIONS

Mosquitoes were abundant in the China-Myanmar bordering agricultural area with clear seasonality. Species composition and density were strongly affected by natural environments. The targeted intervention strategy should be developed and implemented so as to achieve cost-effectiveness for malaria control and elimination along the border areas.