Describing interaction effect between lagged rainfalls on malaria: an epidemiological study in south-west China

Time lag effect on malaria transmission dynamics in an Amazonian Colombian municipality and importance for early warning systems

Malaria remains a significant public health problem worldwide, particularly in low-income regions with limited access to healthcare. Despite the use of antimalarial drugs, transmission remains an issue in Colombia, especially among indigenous populations in remote areas. In this study, we used an SIR Ross MacDonald model that considered land use change, temperature, and precipitation to analyze eco epidemiological parameters and the impact of time lags on malaria transmission in La Pedrera-Amazonas municipality. We found changes in land use between 2007 and 2020, with increases in forested areas, urban infrastructure and water edges resulting in a constant increase in mosquito carrying capacity. Temperature and precipitation variables exhibited a fluctuating pattern that corresponded to rainy and dry seasons, respectively and a marked influence of the El Niño climatic phenomenon. Our findings suggest that elevated precipitation and temperature increase malaria infection risk in the following 2 months. The risk is influenced by the secondary vegetation and urban infrastructure near primary forest formation or water body edges. These results may help public health officials and policymakers develop effective malaria control strategies by monitoring precipitation, temperature, and land use variables to flag high-risk areas and critical periods, considering the time lag effect.

Spatial distribution and temporal dynamics of invasive and native mosquitoes in a large Mediterranean city

Mosquitoes, including invasive species like the Asian tiger mosquito Aedes albopictus, alongside native species Culex pipiens s.l., pose a significant nuisance to humans and serve as vectors for mosquito-borne diseases in urban areas. Understanding the impact of water infrastructure characteristics, climatic conditions, and management strategies on mosquito occurrence and effectiveness of control measures to assess their implications on mosquito occurrence is crucial for effective vector control. In this study, we examined data collected during the local vector control program in Barcelona, Spain, focusing on 234,225 visits to 31,334 different sewers, as well as 1817 visits to 152 fountains between 2015 and 2019. We investigated both the colonization and recolonization processes of mosquito larvae within these water infrastructures. Our findings revealed higher larval presence in sandbox-sewers compared to siphonic or direct sewers, and the presence of vegetation and the use of naturalized water positively influenced larval occurrence in fountains. The application of larvicidal treatment significantly reduced larvae presence; however, recolonization rates were negatively affected by the time elapsed since treatment. Climatic conditions played a critical role in the colonization and recolonization of sewers and urban fountains, with mosquito occurrence exhibiting non-linear patterns and, generally, increasing at intermediate temperatures and accumulated rainfall levels. This study emphasizes the importance of considering sewers and fountains characteristics and climatic conditions when implementing vector control programs to optimize resources and effectively reduce mosquito populations.

The relationship between climate change and malaria in South-East Asia: A systematic review of the evidence

Background: Climatic change is an inescapable fact that implies alterations in seasons where weather occurrences have their schedules shift from the regular and magnitudes intensify to more extreme variations over a multi-year period. Southeast Asia is one of the many regions experiencing changes in climate and concurrently still has endemicities of malaria. Given that previous studies have suggested the influence of climate on malaria's vector the Anopheles mosquitoes and parasite the Plasmodium group, this study was conducted to review the evidence of associations made between malaria cases and climatic variables in Southeast Asia throughout a multi-year period. Methods: Our systematic literature review was informed by the PRISMA guidelines and registered in PROSPERO: CRD42022301826 on 5 th February 2022. We searched for original articles in English and Indonesian that focused on the associations between climatic variables and malaria cases. Results: The initial identification stage resulted in 535 records of possible relevance and after abstract screening and eligibility assessment we included 19 research articles for the systematic review. Based on the reviewed articles, changing temperatures, precipitation, humidity and windspeed were considered for statistical association across a multi-year period and are correlated with malaria cases in various regions throughout Southeast Asia. Conclusions: According to the review of evidence, climatic variables that exhibited a statistically significant correlation with malaria cases include temperatures, precipitation, and humidity. The strength of each climatic variable varies across studies. Our systematic review of the limited evidence indicates that further research for the Southeast Asia region remains to be explored.

An investigation of the efficacy of different statistical models in malaria forecasting in the semi-arid regions of Gujarat, India

BACKGROUND & OBJECTIVES

Robust forecasting of malaria cases is desirable as we are approaching towards malaria elimination in India. Methods enabling robust forecasting and timely case detection in unstable transmission areas are the need of the hour.

METHODS

Forecasting efficacy of the eight most prominent statistical models that are based on three statistical methods: Generalized linear model (Model A and Model B), Smoothing method (Model C), and SARIMA (Model D to model H) were compared using last twelve years (2008-19) monthly malaria data of two districts (Kheda and Anand) of Gujarat state of India.

RESULTS

The SARIMA Model F was found the most appropriate when forecasted for 2017 and 2018 using model-building data sets 1 and 2, respectively, for both the districts: Kheda and Anand. Model H followed by model C were the two models found appropriate in terms of point estimates for 2019. Still, we regretted these two because confidence intervals from these models are wider that they do not have any forecasting utility. Model F is the third one in terms of point prediction but gives a relatively better confidence interval. Therefore, model F was considered the most appropriate for the year 2019 for both districts.

INTERPRETATION & CONCLUSION

Model F was found relatively more appropriate than others and can be used to forecast malaria cases in both districts.

Bayesian Geostatistical Modeling to Assess Malaria Seasonality and Monthly Incidence Risk in Eswatini

Eswatini is on the brink of malaria elimination and had however, had to shift its target year to eliminate malaria on several occasions since 2015 as the country struggled to achieve its zero malaria goal. We conducted a Bayesian geostatistical modeling study using malaria case data. A Bayesian distributed lags model (DLM) was implemented to assess the effects of seasonality on cases. A second Bayesian model based on polynomial distributed lags was implemented on the dataset to improve understanding of the lag effect of environmental factors on cases. Results showed that malaria increased during the dry season with proportion 0.051 compared to the rainy season with proportion 0.047 while rainfall of the preceding month (Lag2) had negative effect on malaria as it decreased by proportion − 0.25 (BCI: − 0.46, − 0.05). Night temperatures of the preceding first and second month were significantly associated with increased malaria in the following proportions: at Lag1 0.53 (BCI: 0.23, 0.84) and at Lag2 0.26 (BCI: 0.01, 0.51). Seasonality was an important predictor of malaria with proportion 0.72 (BCI: 0.40, 0.98). High malaria rates were identified for the months of July to October, moderate rates in the months of November to February and low rates in the months of March to June. The maps produced support-targeted malaria control interventions. The Bayesian geostatistical models could be extended for short-term and long-term forecasting of malaria supporting-targeted response both in space and time for effective elimination.

Climate Change and Vector-Borne Diseases in China: A Review of Evidence and Implications for Risk Management

Simple Summary

Vector-borne diseases are among the most rapidly spreading infectious diseases and are widespread all around the world. In China, many types of vector-borne diseases have been prevalent in different regions, which is a serious public health problem with significant association with meteorological factors and weather events. Under the background of current severe climate change, the outbreaks and transmission of vector-borne diseases have been proven to be impacted greatly due to rapidly changing weather conditions. This study summarizes research progress on the association between climate conditions and all types of vector-borne diseases in China. A total of seven insect-borne diseases, two rodent-borne diseases, and a snail-borne disease were included, among which dengue fever is the most concerning mosquito-borne disease. Temperature, rainfall, and humidity have the most significant effect on vector-borne disease transmission, while the association between weather conditions and vector-borne diseases shows vast differences in China. We also make suggestions about future research based on a review of current studies.

Abstract

Vector-borne diseases have posed a heavy threat to public health, especially in the context of climate change. Currently, there is no comprehensive review of the impact of meteorological factors on all types of vector-borne diseases in China. Through a systematic review of literature between 2000 and 2021, this study summarizes the relationship between climate factors and vector-borne diseases and potential mechanisms of climate change affecting vector-borne diseases. It further examines the regional differences of climate impact. A total of 131 studies in both Chinese and English on 10 vector-borne diseases were included. The number of publications on mosquito-borne diseases is the largest and is increasing, while the number of studies on rodent-borne diseases has been decreasing in the past two decades. Temperature, precipitation, and humidity are the main parameters contributing to the transmission of vector-borne diseases. Both the association and mechanism show vast differences between northern and southern China resulting from nature and social factors. We recommend that more future research should focus on the effect of meteorological factors on mosquito-borne diseases in the era of climate change. Such information will be crucial in facilitating a multi-sectorial response to climate-sensitive diseases in China.

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.

The impacts of precipitation patterns on dengue epidemics in Guangzhou city

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67-55.50 mm per week, Pre_LMD of 3-4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

Effect of temperature and its interactions with relative humidity and rainfall on malaria in a temperate city Suzhou, China

Malaria is a climate-sensitive infectious disease. Many ecological studies have investigated the independent impacts of ambient temperature on malaria. However, the optimal temperature measures of malaria and its interaction with other meteorological factors on malaria transmission are less understood. This study aims to investigate the effect of ambient temperature and its interactions with relative humidity and rainfall on malaria in Suzhou, a temperate climate city in Anhui Province, China. Weekly malaria and meteorological data from 2005 to 2012 were obtained for Suzhou. A distributed lag nonlinear model was conducted to quantify the effect of different temperature measures on malaria. The best measure was defined as that with the minimum quasi-Akaike information criterion. GeoDetector and Poisson regression models were employed to quantify the interactions of temperature, relative humidity, and rainfall on malaria transmission. A total of 13,382 malaria cases were notified in Suzhou from 2005 to 2012. Each 5 °C rise in average temperature over 10 °C resulted in a 22% (95% CI: 17%, 28%) increase in malaria cases at lag of 4 weeks. In terms of cumulative effects from lag 1 to 8 weeks, each 5 °C increase over 10 °C caused a 175% growth in malaria cases (95% CI: 139%, 216%). Average temperature achieved the best performance in terms of model fitting, followed by minimum temperature, most frequent temperature, and maximum temperature. Temperature had an interactive effect on malaria with relative humidity and rainfall. High temperature together with high relative humidity and high rainfall could accelerate the transmission of malaria. Meteorological factors may affect malaria transmission interactively. The research findings could be helpful in the development of weather-based malaria early warning system, especially in the context of climate change for the prevention of possible malaria resurgence.

Disparities in Risks of Malaria Associated with Climatic Variability among Women, Children and Elderly in the Chittagong Hill Tracts of Bangladesh

Malaria occurrence in the Chittagong Hill Tracts in Bangladesh varies by season and year, but this pattern is not well characterized. The role of environmental conditions on the occurrence of this vector-borne parasitic disease in the region is not fully understood. We extracted information on malaria patients recorded in the Upazila (sub-district) Health Complex patient registers of Rajasthali in Rangamati district of Bangladesh from February 2000 to November 2009. Weather data for the study area and period were obtained from the Bangladesh Meteorological Department. Non-linear and delayed effects of meteorological drivers, including temperature, relative humidity, and rainfall on the incidence of malaria, were investigated. We observed significant positive association between temperature and rainfall and malaria occurrence, revealing two peaks at 19 °C (logarithms of relative risks (logRR) = 4.3, 95% CI: 1.1-7.5) and 24.5 °C (logRR = 4.7, 95% CI: 1.8-7.6) for temperature and at 86 mm (logRR = 19.5, 95% CI: 11.7-27.3) and 284 mm (logRR = 17.6, 95% CI: 9.9-25.2) for rainfall. In sub-group analysis, women were at a much higher risk of developing malaria at increased temperatures. People over 50 years and children under 15 years were more susceptible to malaria at increased rainfall. The observed associations have policy implications. Further research is needed to expand these findings and direct resources to the vulnerable populations for malaria prevention and control in the Chittagong Hill Tracts of Bangladesh and the region with similar settings.

Nonlinear effect of wind velocity on mumps in Shenzhen, China, 2013-2016

OBJECTIVES

Plenty of studies have shown that wind velocity has an influence on airborne diseases. There is, however, no consistent conclusion found on the relationship between wind velocity and mumps, and the regional heterogeneity has been largely neglected in previous studies. This study aims to explore the association between wind velocity and mumps in Shenzhen.

STUDY DESIGN

Ecological study.

METHODS

Sixteen subdistricts with the highest incidence rates of mumps were selected from Shenzhen city, and the multilevel distributed lag-nonlinear model was conducted to explore the relationship between mumps cases and wind velocity via the dlnm and lme4 packages of the software R 3.4.3.

RESULTS

In Shenzhen, a total of 16,997 mumps cases were reported between 2013 and 2016, and the means of daily rainfall, temperature, relative humidity, and 10 min wind velocity were 5.74 mm, 23.27 °C, 76.31% and 1.87 m/s, respectively. Obvious nonlinear correlation relationships of wind velocity and mumps risk were found, where a reverse-V curved shape was shown in the exposure dimension with the logRR value of mumps peaking at 2 m/s, and the type of nonlinear correlation varying with the levels of wind velocity in lag dimension with a peak at two lag weeks.

CONCLUSIONS

The lag and nonlinear association between wind velocity and number of mumps cases were examined, while there was no statistically significant associations for other meteorological factors accounting for the regional heterogeneity. Results from this study indicated that public health administrators could strengthen health education in schools on ventilation management to prevent and control mumps outbreaks.

Effects of time-lagged meteorological variables on attributable risk of leishmaniasis in central region of Afghanistan

BACKGROUND

Leishmaniasis remains one of the world's most neglected vector-borne diseases, affecting predominantly poor communities mainly in developing countries. Previous studies have shown that the distribution and dynamics of leishmaniasis infections are sensitive to environmental factors, however, there are no studies on the burden of leishmaniasis attributable to time-varying meteorological variables.

METHODS

This study used data from 3 major leishmaniosis afflicted provinces of Afghanistan, between 2003 and 2009, to provide empirical analysis of change in heat/cold-leishmaniosis association. Non-linear and delayed exposure-lag-response relationship between meteorological variables and leishmaniasis were fitted with a distributed lag non-linear model applying a spline function which describes the dependency along the range of values with a lag of up to 12 months. We estimated the risk of leishmaniasis attributable to high and low temperature.

RESULTS

The median monthly mean temperature and rainfall were 16.1 °C and 0.6 in., respectively. Seasonal variations of leishmaniasis were consistent between males and females, however significant differences were observed among different age groups. Temperature effects were immediate and persistent (lag 0-12 months). The cumulative risks were highest at cold temperatures. The cumulative relative risks (logRR) for leishmaniasis were 6.16 (95% CI: 5.74-6.58) and 1.15 (95% CI: 1.32-1.31) associated with the 10th percentile temperature (2.16 °C) and the 90th percentile temperature (28.46 °C). The subgroup analysis showed increased risk for males as well as young and middle aged people at cold temperatures, however, higher risk was observed for the elderly in heat. The overall leishmaniasis-temperature attributable fractions was estimated to be 7.6% (95% CI: 7.5%-7.7%) and mostly due to cold.

CONCLUSION

Findings in this study highlight the non-linearity, delay of effects and magnitude of leishmaniasis risk associated with temperature. The disparity of risk between different subgroups can hopefully advise policy makers and assist in leishmaniasis control program.

Health impact of climate change in cities of middle-income countries: the case of China

BACKGROUND

This review examines the human health impact of climate change in China. Through reviewing available research findings under four major climate change phenomena, namely extreme temperature, altered rainfall pattern, rise of sea level and extreme weather events, relevant implications for other middle-income population with similar contexts will be synthesized.

SOURCES OF DATA

Sources of data included bilingual peer-reviewed articles published between 2000 and 2018 in PubMed, Google Scholar and China Academic Journals Full-text Database.

AREAS OF AGREEMENT

The impact of temperature on mortality outcomes was the most extensively studied, with the strongest cause-specific mortality risks between temperature and cardiovascular and respiratory mortality. The geographical focuses of the studies indicated variations in health risks and impacts of different climate change phenomena across the country.

AREAS OF CONTROVERSY

While rainfall-related studies predominantly focus on its impact on infectious and vector-borne diseases, consistent associations were not often found.

GROWING POINTS

Mental health outcomes of climate change had been gaining increasing attention, particularly in the context of extreme weather events. The number of projection studies on the long-term impact had been growing.

AREAS TIMELY FOR DEVELOPING RESEARCH

The lack of studies on the health implications of rising sea levels and on comorbidity and injury outcomes warrants immediate attention. Evidence is needed to understand health impacts on vulnerable populations living in growing urbanized cities and urban enclaves, in particular migrant workers. Location-specific climate-health outcome thresholds (such as temperature-mortality threshold) will be needed to support evidence-based clinical management plans and health impact mitigation strategies to protect vulnerable communities.

The impact of climate variability on infectious disease transmission in China: Current knowledge and further directions

BACKGROUND

Climate change may lead to emerging and re-emerging infectious diseases and pose public health challenges to human health and the already overloaded healthcare system. It is therefore important to review current knowledge and identify further directions in China, the largest developing country in the world.

METHODS

A comprehensive literature review was conducted to examine the relationship between climate variability and infectious disease transmission in China in the new millennium. Literature was identified using the following MeSH terms and keywords: climatic variables [temperature, precipitation, rainfall, humidity, etc.] and infectious disease [viral, bacterial and parasitic diseases].

RESULTS

Fifty-eight articles published from January 1, 2000 to May 30, 2018 were included in the final analysis, including bacterial diarrhea, dengue, malaria, Japanese encephalitis, HFRS, HFMD, Schistosomiasis. Each 1 °C rise may lead to 3.6%-14.8% increase in the incidence of bacillary dysentery disease in south China. A 1 °C rise was corresponded to an increase of 1.8%-5.9% in the weekly notified HFMD cases in west China. Each 1 °C rise of temperature, 1％ rise in relative humidity and one hour rise in sunshine led to an increase of 0.90%, 3.99% and 0.68% in the monthly malaria cases, respectively. Climate change with the increased temperature and irregular patterns of rainfall may affect the pathogen reproduction rate, their spread and geographical distribution, change human behavior and influence the ecology of vectors, and increase the rate of disease transmission in different regions of China.

CONCLUSION

Exploring relevant adaptation strategies and the health burden of climate change will assist public health authorities to develop an early warning system and protect China's population health, especially in the new 1.5 °C scenario of the newly released IPCC special report.

Socioeconomic and environmental factors associated with malaria hotspots in the Nanoro demographic surveillance area, Burkina Faso

BACKGROUND

With limited resources and spatio-temporal heterogeneity of malaria in developing countries, it is still difficult to assess the real impact of socioeconomic and environmental factors in order to set up targeted campaigns against malaria at an accurate scale. Our goal was to detect malaria hotspots in rural area and assess the extent to which household socioeconomic status and meteorological recordings may explain the occurrence and evolution of these hotspots.

METHODS

Data on malaria cases from 2010 to 2014 and on socioeconomic and meteorological factors were acquired from four health facilities within the Nanoro demographic surveillance area. Statistical cross correlation was used to quantify the temporal association between weekly malaria incidence and meteorological factors. Local spatial autocorrelation analysis was performed and restricted to each transmission period using Kulldorff's elliptic spatial scan statistic. Univariate and multivariable analysis were used to assess the principal socioeconomic and meteorological determinants of malaria hotspots using a Generalized Estimating Equation (GEE) approach.

RESULTS

Rainfall and temperature were positively and significantly associated with malaria incidence, with a lag time of 9 and 14 weeks, respectively. Spatial analysis showed a spatial autocorrelation of malaria incidence and significant hotspots which was relatively stable throughout the study period. Furthermore, low socioeconomic status households were strongly associated with malaria hotspots (aOR = 1.21, 95% confidence interval: 1.03-1.40).

CONCLUSION

These fine-scale findings highlight a relatively stable spatio-temporal pattern of malaria risk and indicate that social and environmental factors play an important role in malaria incidence. Integrating data on these factors into existing malaria struggle tools would help in the development of sustainable bottleneck strategies adapted to the local context for malaria control.

Statistical modeling of the effect of rainfall flushing on dengue transmission in Singapore

BACKGROUND

Rainfall patterns are one of the main drivers of dengue transmission as mosquitoes require standing water to reproduce. However, excess rainfall can be disruptive to the Aedes reproductive cycle by "flushing out" aquatic stages from breeding sites. We developed models to predict the occurrence of such "flushing" events from rainfall data and to evaluate the effect of flushing on dengue outbreak risk in Singapore between 2000 and 2016.

METHODS

We used machine learning and regression models to predict days with "flushing" in the dataset based on entomological and corresponding rainfall observations collected in Singapore. We used a distributed lag nonlinear logistic regression model to estimate the association between the number of flushing events per week and the risk of a dengue outbreak.

RESULTS

Days with flushing were identified through the developed logistic regression model based on entomological data (test set accuracy = 92%). Predictions were based upon the aggregate number of thresholds indicating unusually rainy conditions over multiple weeks. We observed a statistically significant reduction in dengue outbreak risk one to six weeks after flushing events occurred. For weeks with five or more flushing events, compared with weeks with no flushing events, the risk of a dengue outbreak in the subsequent weeks was reduced by 16% to 70%.

CONCLUSIONS

We have developed a high accuracy predictive model associating temporal rainfall patterns with flushing conditions. Using predicted flushing events, we have demonstrated a statistically significant reduction in dengue outbreak risk following flushing, with the time lag well aligned with time of mosquito development from larvae and infection transmission. Vector control programs should consider the effects of hydrological conditions in endemic areas on dengue transmission.

Decline in malaria incidence in a typical county of China: Role of climate variance and anti-malaria intervention measures

Malaria is an important vector-borne disease which is widespread in tropical and subtropical areas worldwide as well as in south China. Previous research has separately focused on the association between malaria incidence and meteorological variables or between malaria incidence and anti-malaria intervention measures in China, especially in Yunnan Province. Therefore, a typical county, Tengchong County, in Yunnan Province with high malaria incidence was selected as the study area to investigate the integrated influence of climate variance and anti-malaria intervention measures. Malaria incidence and meteorological variables were analyzed with a 2-month lag. The variables include average monthly temperature, minimum temperature, maximum temperature, cumulative precipitation, wind speed, maximum wind speed, relative humidity and minimum relative humidity. First, the principal component analysis was introduced to investigate the relationship between malaria incidence and meteorological variables; classification and regression trees were used to clarify contributions of key meteorological variables to malaria incidence afterwards. Second, based on existing anti-malaria intervention measures and above results, the integrated impact of climate variance and anti-malaria interventions on interannual trends of malaria incidence was analyzed. High malaria incidence occurred under one of the two meteorological conditions: 1) high minimum temperature combined with high minimum relative humidity or both precipitation and minimum relative humidity above middle level; 2) middle minimum temperature combined with both precipitation and minimum relative humidity below middle levels. Moreover, the steep interannual decline of malaria incidence in Tengchong was determined by slight climate variance and persistent anti-malaria intervention measures during malaria epidemics, predominantly by the latter. These findings will provide evidence data for developing malaria surveillance strategies in China.

Malaria transmission trends and its lagged association with climatic factors in the highlands of Plateau State, Nigeria

Background:

Malaria is a serious disease and still remains a public health problem in many parts of Nigeria.

Objectives:

The aim of this study was to describe malaria transmission trends and analyzed the impact of climatic factors on malaria transmission in the highlands of Plateau State, Central Nigeria.

Methods:

The study was a retrospective survey which used archival data of climate parameters and medical case records on malaria. Rainfall, relative humidity, and temperature data were obtained from the nearest weather stations to the study locations from 1980 to 2015. Data on reported malaria cases were collected from general hospitals in the selected local government areas (LGAs) from 2003 to 2015. Generalized Additive Models were used to model trends in malaria incidences over time, and it is lagged association with climatic factors.

Results:

The results show a significant cyclical trend in malaria incidence in all the study areas (P < 0.001). The association between monthly malaria cases and mean monthly temperature, rainfall, and relative humidity show significant association at different time lags and locations.

Conclusion:

Our findings suggest that climatic factors are among the major determinants of malaria transmission in the highlands of Plateau state except in Jos-North LGA where the low model deviance explained (35.4%) could mean that there are other important factors driving malaria transmission in the area other than climatic factors.

Spatio-temporal modelling of weekly malaria incidence in children under 5 for early epidemic detection in Mozambique

Malaria is a major cause of morbidity and mortality in Mozambique. We present a malaria early warning system (MEWS) for Mozambique informed by seven years of weekly case reports of malaria in children under 5 years of age from 142 districts. A spatio-temporal model was developed based on explanatory climatic variables to map exceedance probabilities, defined as the predictive probability that the relative risk of malaria incidence in a given district for a particular week will exceed a predefined threshold. Unlike most spatially discrete models, our approach accounts for the geographical extent of each district in the derivation of the spatial covariance structure to allow for changes in administrative boundaries over time. The MEWS can thus be used to predict areas that may experience increases in malaria transmission beyond expected levels, early enough so that prevention and response measures can be implemented prior to the onset of outbreaks. The framework we present is also applicable to other climate-sensitive diseases.