Correlation of Climate Variability and Malaria: A Retrospective Comparative Study, Southwest Ethiopia

Urban climate and cardiovascular health: Focused on seasonal variation of urban temperature, relative humidity, and PM2.5 air pollution

Seasonal effects on subclinical cardiovascular functions (CVFs) are an important emerging health issue for people living in urban environment. The objectives of this study were to demonstrate the effects of seasonal variations of temperature, relative humidity, and PM2.5 air pollution on CVFs. A total of 86 office workers in Taipei City were recruited, their arterial pressure waveform was recorded by cuff sphygmomanometer using an oscillometric blood pressure (BP) device for CVFs assessment. Results of paried t-test with Bonferroni correction showed significantly increased systolic and diastolic BP (SBP, DBP), central end-systolic and diastolic BP (cSBP, cDBP) and systemic vascular resistance, but decreased heart rate (HR), stroke volume (SV), cardio output (CO), and cardiac index in winter compared with other seasons. After controlling for related confounding factors, SBP, DBP, cSBP, cDBP, LV dp/dt max, and brachial-ankle pulse wave velocity (baPWV) were negatively associated with, and SV was positively associated with seasonal temperature changes. Seasonal changes of air pollution in terms of PM2.5 were significantly positively associated with DBP and cDBP, as well as negatively associated with HR and CO. Seasonal changes of relative humidity were significantly negatively associated with DBP, and cDBP, as well as positively associated with HR, CO, and baPWV. This study provides evidence of greater susceptibility to cardiovascular events in winter compared with other seasons, with ambient temperature, relative humidity, and PM2.5 as the major factors of seasonal variation of CVFs.

Climate parameter and malaria association in north-east India

This study was performed in order to understand the effect of climatological variables on the malaria situation in the north-east region of India, which is prolonged by the disease. Time-series analysis of major climate parameters like rainfall, maximum temperature, minimum temperature, mean temperature, relative humidity, and soil moisture distributions is carried out, and their correlation with the malaria incidence is quantified state-wise, which is the unique part of the study. The correlation analysis reveals that malaria is significantly related with the maximum temperature and soil moisture in three out of eight states in NE India. To assess the climate variability, the inter-dependency between the meteorological parameters is obtained and the state wise correlation matrix for all states are reported. The analysis shows that maximum and mean temperature has highest positive correlation whereas minimum temperature and relative humidity has negative correlation. The climate-malaria relation is being carried out in the study region using the regression analysis and the results revealed that the regional climate has the most impact for the malaria incidence in the state of Arunachal Pradesh, Meghalaya, Tripura and Nagaland and in other states the impact is moderate. Analysis of variance modelling in the regions also indicates the degree of the fitment of both the data sets with the regression model and it is observed that the relation is also significant in the same 4 states. As a case study the impact of large scale oscillations like El Niño-Southern Oscillation on the malaria load is also assessed which can be a good indicator in the prediction of the climate and in turn the malaria incidences over the region.

Public health priorities for Sino-Africa cooperation in Eastern Africa in context of flooding and malaria burden in Children: a tridecadal retrospective analysis

BACKGROUND

Malaria remains a major public health burden to children under five, especially in Eastern Africa (E.A), -a region that is also witnessing the increasing occurrence of floods and extreme climate change. The present study, therefore, explored the trends in floods, as well as the association of their occurrence and duration with the malaria incidence in children < 5 years in five E.A partner countries of Forum for China-Africa Cooperation (FOCAC), including Ethiopia, Kenya, Somalia, Sudan, and Tanzania between 1990 and 2019.

METHODS

A retrospective analysis of data retrieved from two global sources was performed: the Emergency Events Database (EM-DAT) and the Global Burden of Diseases Study (GBD) between 1990 and 2019. Using SPSS 20.0, a correlation was determined based on ρ= -1 to + 1, as well as the statistical significance of P = < 0.05. Time plots of trends in flooding and malaria incidence were generated in 3 different decades using R version 4.0.

RESULTS

Between 1990 and 2019, the occurrence and duration of floods among the five E.A partner countries of FOCAC increased and showed an upward trend. On the contrary, however, this had an inverse and negative, as well as a weak correlation on the malaria incidence in children under five years. Only Kenya, among the five countries, showed a perfect negative correction of malaria incidence in children under five with flood occurrence (ρ = -0.586**, P-value = 0.001) and duration (ρ = -0.657**, P-value = < 0.0001).

CONCLUSIONS

This study highlights the need for further research to comprehensively explore how different climate extreme events, which oftentimes complement floods, might be influencing the risk of malaria in children under five in five E.A malaria-endemic partner countries of FOCAC. Similarly, it ought to consider investigating the influence of other attributes apart from flood occurrence and duration, which also compound floods like displacement, malnutrition, and water, sanitation and hygiene on the risk and distribution of malaria and other climate-sensitive diseases.

A world free of malaria: It is time for Africa to actively champion and take leadership of elimination and eradication strategies

The global burden of malaria seems unabated. Africa carries the greatest burden accounting for over 95% of the annual cases of malaria. For the vision of a world free of malaria by Global Technical Strategy to be achieved, Africa must take up the stakeholder's role. It is therefore imperative that Africa rises up to the challenge of malaria and champion the fight against it. The fight against malaria may just be a futile or mere academic venture if Africans are not directly and fully involved. This work reviews the roles playable by Africans in order to curb the malaria in Africa and the world at large.

Predicting malaria outbreaks from sea surface temperature variability up to 9 months ahead in Limpopo, South Africa, using machine learning

Malaria is the cause of nearly half a million deaths worldwide each year, posing a great socioeconomic burden. Despite recent progress in understanding the influence of climate on malaria infection rates, climatic sources of predictability remain poorly understood and underexploited. Local weather variability alone provides predictive power at short lead times of 1-2 months, too short to adequately plan intervention measures. Here, we show that tropical climatic variability and associated sea surface temperature over the Pacific and Indian Oceans are valuable for predicting malaria in Limpopo, South Africa, up to three seasons ahead. Climatic precursors of malaria outbreaks are first identified via lag-regression analysis of climate data obtained from reanalysis and observational datasets with respect to the monthly malaria case count data provided from 1998-2020 by the Malaria Institute in Tzaneen, South Africa. Out of 11 sea surface temperature sectors analyzed, two regions, the Indian Ocean and western Pacific Ocean regions, emerge as the most robust precursors. The predictive value of these precursors is demonstrated by training a suite of machine-learning classification models to predict whether malaria case counts are above or below the median historical levels and assessing their skills in providing early warning predictions of malaria incidence with lead times ranging from 1 month to a year. Through the development of this prediction system, we find that past information about SST over the western Pacific Ocean offers impressive prediction skills (~80% accuracy) for up to three seasons (9 months) ahead. SST variability over the tropical Indian Ocean is also found to provide good skills up to two seasons (6 months) ahead. This outcome represents an extension of the effective prediction lead time by about one to two seasons compared to previous prediction systems that were more computationally costly compared to the machine learning techniques used in the current study. It also demonstrates the value of climatic information and the prediction framework developed herein for the early planning of interventions against malaria outbreaks.

Spatio-temporal variability of malaria infection in Chahbahar County, Iran: association with the ENSO and rainfall variability

Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.

Effects of rainfall, temperature and topography on malaria incidence in elimination targeted district of Ethiopia

BACKGROUND

Climate and environmental factors could be one of the primary factors that drive malaria transmission and it remains to challenge the malaria elimination efforts. Hence, this study was aimed to evaluate the effects of meteorological factors and topography on the incidence of malaria in the Boricha district in Sidama regional state of Ethiopia.

METHODS

Malaria morbidity data recorded from 2010 to 2017 were obtained from all public health facilities of Boricha District in the Sidama regional state of Ethiopia. The monthly malaria cases, rainfall, and temperature (minimum, maximum, and average) were used to fit the ARIMA model to compute the malaria transmission dynamics and also to forecast future incidence. The effects of the meteorological variables and altitude were assessed with a negative binomial regression model using R version 4.0.0. Cross-correlation analysis was employed to compute the delayed effects of meteorological variables on malaria incidence.

RESULTS

Temperature, rainfall, and elevation were the major determinants of malaria incidence in the study area. A regression model of previous monthly rainfall at lag 0 and Lag 2, monthly mean maximum temperature at lag 2 and Lag 3, and monthly mean minimum temperature at lag 3 were found as the best prediction model for monthly malaria incidence. Malaria cases at 1801-1900 m above sea level were 1.48 times more likely to occur than elevation ≥ 2000 m.

CONCLUSIONS

Meteorological factors and altitude were the major drivers of malaria incidence in the study area. Thus, evidence-based interventions tailored to each determinant are required to achieve the malaria elimination target of the country.

Past eight-year malaria data in Gedeo zone, southern Ethiopia: trend, reporting-quality, spatiotemporal distribution, and association with socio-demographic and meteorological variables

Background

Informed decision making is underlined by all tiers in the health system. Poor data record system coupled with under- (over)-reporting of malaria cases affects the country’s malaria elimination activities. Thus, malaria data at health facilities and health offices are important particularly to monitor and evaluate the elimination progresses. This study was intended to assess overall reported malaria cases, reporting quality, spatiotemporal trends and factors associated in Gedeo zone, South Ethiopia.

Methods

Past 8 years retrospective data stored in 17 health centers and 5 district health offices in Gedeo Zone, South Ethiopia were extracted. Malaria cases data at each health center with sociodemographic information, between January 2012 and December 2019, were included. Meteorological data were obtained from the national meteorology agency of Ethiopia. The data were analyzed using Stata 13.

Results

A total of 485,414 suspected cases were examined for malaria during the previous 8 years at health centers. Of these suspects, 57,228 (11.79%) were confirmed malaria cases with an overall decline during the 8-year period. We noted that 3758 suspected cases and 467 confirmed malaria cases were not captured at the health offices. Based on the health centers records, the proportions of Plasmodium falciparum (49.74%) and P. vivax (47.59%) infection were nearly equivalent (p = 0.795). The former was higher at low altitudes while the latter was higher at higher altitudes. The over 15 years of age group accounted for 11.47% of confirmed malaria cases (p < 0.001). There was high spatiotemporal variation: the highest case record was during Belg (12.52%) and in Dilla town (18,150, 13.17%, p < 0.001) which is located at low altitude. Monthly rainfall and minimum temperature exhibited strong associations with confirmed malaria cases.

Conclusion

A notable overall decline in malaria cases was observed during the eight-year period. Both P. falciparum and P. vivax were found at equivalent endemicity level; hence control measures should continue targeting both species. The noticed under reporting, the high malaria burden in urban settings, low altitudes and Belg season need spatiotemporal consideration by the elimination program.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-021-05783-8.

Near-term climate change impacts on sub-national malaria transmission

The role of climate change on global malaria is often highlighted in World Health Organisation reports. We modelled a Zambian socio-environmental dataset from 2000 to 2016, against malaria trends and investigated the relationship of near-term environmental change with malaria incidence using Bayesian spatio-temporal, and negative binomial mixed regression models. We introduced the diurnal temperature range (DTR) as an alternative environmental measure to the widely used mean temperature. We found substantial sub-national near-term variations and significant associations with malaria incidence-trends. Significant spatio-temporal shifts in DTR/environmental predictors influenced malaria incidence-rates, even in areas with declining trends. We highlight the impact of seasonally sensitive DTR, especially in the first two quarters of the year and demonstrate how substantial investment in intervention programmes is negatively impacted by near-term climate change, most notably since 2010. We argue for targeted seasonally-sensitive malaria chemoprevention programmes.

Multi-cluster and environmental dependant vector born disease models

Vector-born disease models are extensively used for surveillance and control processes. The most simple and generally use model (SEIR-SEI model) cannot explain a variety of phenomena involved in these diseases spread and development. In order to obtain a wider insight of the vector-born disease models (and the dynamics involved in them), this work focuses into analyse the classical model, a modified versions of it, and 8 their parameters. The modified version includes host mobility, 9 environmental, re-susceptibility, and mosquito life cycle considerations. As results it is observed that there are a limiting number of parameters that play the most important roles in the dynamics (those related to mortality rates, recovery rate from infectious, and pathogen transmission probabilities). Therefore, parameters determination should focus primarily into estimate these values. Stronger effects of the environmental variables are observed and expected by using different parameters and/or the use of multiple environmental variable at the same time.

Statistical Modelling of the Effects of Weather Factors on Malaria Occurrence in Abuja, Nigeria

Background: despite the increase in malaria control and elimination efforts, weather patterns and ecological factors continue to serve as important drivers of malaria transmission dynamics. This study examined the statistical relationship between weather variables and malaria incidence in Abuja, Nigeria. Methodology/Principal Findings: monthly data on malaria incidence and weather variables were collected in Abuja from the year 2000 to 2013. The analysis of count outcomes was based on generalized linear models, while Pearson correlation analysis was undertaken at the bivariate level. The results showed more malaria incidence in the months with the highest rainfall recorded (June–August). Based on the negative binomial model, every unit increase in humidity corresponds to about 1.010 (95% confidence interval (CI), 1.005–1.015) times increase in malaria cases while the odds of having malaria decreases by 5.8% for every extra unit increase in temperature: 0.942 (95% CI, 0.928–0.956). At lag 1 month, there was a significant positive effect of rainfall on malaria incidence while at lag 4, temperature and humidity had significant influences. Conclusions: malaria remains a widespread infectious disease among the local subjects in the study area. Relative humidity was identified as one of the factors that influence a malaria epidemic at lag 0 while the biggest significant influence of temperature was observed at lag 4. Therefore, emphasis should be given to vector control activities and to create public health awareness on the proper usage of intervention measures such as indoor residual sprays to reduce the epidemic especially during peak periods with suitable weather conditions.

Investigating climate suitability conditions for malaria transmission and impacts of climate variability on mosquito survival in the humid tropical region: a case study of Obafemi Awolowo University Campus, Ile-Ife, south-western Nigeria

This study investigated impacts of climate variability on mosquito survival at Obafemi Awolowo University Campus, Ile-Ife, south-western Nigeria, and determined the regional climate suitability level for malaria transmission between 1996 and 2015. It employed some established climate-dependent models to simulate daily mosquito survival probabilities, p and a fuzzy logic suitability (FLS) model to determine the suitability conditions for malaria transmission across seasons. Multivariate regression analysis and lag correlation up to 4 months were performed to examine contributions of climate variation to the reported malaria cases. Results revealed that mosquitoes could survive all-year round with p values ranging between 0.40 and 0.96 under the prevailing mean climate. However, the climate suitability level for transmission of malaria was 'moderate' (0.45 < p ≤ 0.60) in the dry season but 'very high' (0.75 < p ≤ 0.96) in the wet. Rainfall was found to be the best predictor (r = 0.7, R² = 0.448, p < 0.05) and no significant time-delay effect was noticed between climatic variables and malaria occurrence except for wind speed at 1-month lag. About 61% (multiple R²= 0.613 at p = 0.1) of monthly variations in reported malaria cases were accounted for by climate variability. Further probe revealed that non-climatic factors such as behavioural and socio-cultural status of the students' population played a very important role in malaria transmission and occurrence. The findings suggested that effective malaria control and interventions must integrate the crucial roles of both climatic and non-climatic factors in the study area.

Plasmodium infection in Kerteszia cruzii (Diptera: Culicidae) in the Atlantic tropical rain forest, southeastern Brazil

In Southeastern Brazil, Kerteszia cruzii (former Anopheles cruzii), a bromeliad mosquito species, is considered an efficient human Plasmodium spp. vector. In this region, recent studies showed asymptomatic or sub-patent Plasmodium falciparum infection. In areas of the Atlantic coast in Rio de Janeiro, Plasmodium simium infection was recently reported in both human and howler monkey. Considering that (1) few malaria cases are reported each year in areas across the tropical Atlantic rain forest in southeastern Brazil; (2) malaria elimination in Atlantic forest is challenged by circulation of P. falciparum and P. simium in humans; (3) the complexity of malaria epidemiology in this region; and (4) the public health importance of Kerteszia cruzii as a sylvatic vector; the major goal of this study is to evaluate Plasmodium infection in Ke. cruzii. Mosquito sampling collections were conducted in Esteiro do Morro and Sítio Itapuan, in Cananeia municipality, and Tapiraí municipality in Ribeira Valley, southeastern São Paulo state, Brazil. Influence of climate and landscape factors in Plasmodium infection in Ke. cruzii was addressed. Among the 1719 mosquitoes tested, 3 females collected in Sítio Itapuan and three from Tapiraí were found infected with either P. vivax or P. simium. Results of statistical analyses did not demonstrate association between Plasmodium infection in mosquito and the landscape. Mosquito infection was found in two landscape clusters, with Plasmodium detected in forest fringe mosquitoes. This finding shows that Ke. cruzii can facilitate transmission among human and non-human primates. Plasmodium falciparum was not identified in the samples analyzed. Spatiotemporal variation in local malaria incidence, low prevalence of Plasmodium, variations in humidity and temperature can explain the absence of mosquitoes infected with P. falciparum in the study.

A comparison of thick-film microscopy, rapid diagnostic test, and polymerase chain reaction for accurate diagnosis of Plasmodium falciparum malaria

Background

Accurate diagnosis of malaria is important for effective disease management and control. In Cameroon, presumptive clinical diagnosis, thick-film microscopy (TFM), and rapid diagnostic tests (RDT) are commonly used to diagnose cases of Plasmodium falciparum malaria. However, these methods lack sensitivity to detect low parasitaemia. Polymerase chain reaction (PCR), on the other hand, enhances the detection of sub-microscopic parasitaemia making it a much-needed tool for epidemiological surveys, mass screening, and the assessment of interventions for malaria elimination. Therefore, this study sought to determine the frequency of cases missed by traditional methods that are detected by PCR.

Methods

Blood samples, collected from 551 febrile Cameroonian patients between February 2014 and February 2015, were tested for P. falciparum by microscopy, RDT and PCR. The hospital records of participants were reviewed to obtain data on the clinical diagnosis made by the health care worker.

Results

The prevalence of malaria by microscopy, RDT and PCR was 31%, 45%, and 54%, respectively. However, of the 92% of participants diagnosed as having clinical cases of malaria by the health care worker, 38% were malaria-negative by PCR. PCR detected 23% and 12% more malaria infections than microscopy and RDT, respectively. A total of 128 (23%) individuals had sub-microscopic infections in the study population. The sensitivity of microscopy, RDT, and clinical diagnosis was 57%, 78% and 100%; the specificity was 99%, 94%, and 17%; the positive predictive values were 99%, 94%, and 59%; the negative predictive values were 66%, 78%, and 100%, respectively. Thus, 41% of the participants clinically diagnosed as having malaria had fever caused by other pathogens.

Conclusions

Malaria diagnostic methods, such as TFM and RDT missed 12–23% of malaria cases detected by PCR. Therefore, traditional diagnostic approaches (TFM, RDT and clinical diagnosis) are not adequate when accurate epidemiological data are needed for monitoring malaria control and elimination interventions.

Exploring the Impact of Climate Variability on Malaria Transmission Using a Dynamic Mosquito-Human Malaria Model

Introduction:

The reasons for malaria resurgence mostly in Africa are yet to be well understood. Although the causes are often linked to regional climate change, it is important to understand the impact of climate variability on the dynamics of the disease. However, this is almost impossible without adequate long-term malaria data over the study areas.

Methods:

In this study, we develop a climate-based mosquito-human malaria model to study malaria dynamics in the human population over KwaZulu-Natal, one of the epidemic provinces in South Africa, from 1970-2005. We compare the model output with available observed monthly malaria cases over the province from September 1999 to December 2003. We further use the model outputs to explore the relationship between the climate variables (rainfall and temperature) and malaria incidence over the province using principal component analysis, wavelet power spectrum and wavelet coherence analysis. The model produces a reasonable fit with the observed data and in particular, it captures all the spikes in malaria prevalence.

Results:

Our results highlight the importance of climate factors on malaria transmission and show the seasonality of malaria epidemics over the province. Results from the principal component analyses further suggest that, there are two principal factors associated with climates variables and the model outputs. One of the factors indicate high loadings on Susceptible, Exposed and Infected human, while the other is more correlated with Susceptible and Recovered humans. However, both factors reveal the inverse correlation between Susceptible-Infected and Susceptible-Recovered humans respectively. Through the spectrum analysis, we notice a strong annual cycle of malaria incidence over the province and ascertain a dominant of one year periodicity. Consequently, our findings indicate that an average of 0 to 120-day lag is generally noted over the study period, but the 120-day lag is more associated with temperature than rainfall. This is consistence with other results obtained from our analyses that malaria transmission is more tightly coupled with temperature than with rainfall in KwaZulu-Natal province.

Modelling Trends of Climatic Variability and Malaria in Ghana Using Vector Autoregression

Malaria is considered endemic in over hundred countries across the globe. Many cases of malaria and deaths due to malaria occur in Sub-Saharan Africa. The disease is of great public health concern since it affects people of all age groups more especially pregnant women and children because of their vulnerability. This study sought to use vector autoregression (VAR) models to model the impact of climatic variability on malaria. Monthly climatic data (rainfall, maximum temperature, and relative humidity) from 2010 to 2015 were obtained from the Ghana Meteorological Agency while data on malaria for the same period were obtained from the Ghana Health Service. Results of the Granger and instantaneous causality tests led to a conclusion that malaria is influenced by all three climatic variables. The impulse response analyses indicated that the highest positive effect of maximum temperature, relative humidity, and rainfall on malaria is observed in the months of September, March, and October, respectively. The decomposition of forecast variance indicates varying degree of malaria dependence on the climatic variables, with as high as 12.65% of the variability in the trend of malaria which has been explained by past innovations in maximum temperature alone. This is quite significant and therefore, policy-makers should not ignore temperature when formulating policies to address malaria.

Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis

The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R² = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention.

Seasonally lagged effects of climatic factors on malaria incidence in South Africa

Globally, malaria cases have drastically dropped in recent years. However, a high incidence of malaria remains in some sub-Saharan African countries. South Africa is mostly malaria-free, but northeastern provinces continue to experience seasonal outbreaks. Here we investigate the association between malaria incidence and spatio-temporal climate variations in Limpopo. First, dominant spatial patterns in malaria incidence anomalies were identified using self-organizing maps. Composite analysis found significant associations among incidence anomalies and climate patterns. A high incidence of malaria during the pre-peak season (Sep-Nov) was associated with the climate phenomenon La Niña and cool air temperatures over southern Africa. There was also high precipitation over neighbouring countries two to six months prior to malaria incidence. During the peak season (Dec-Feb), high incidence was associated with positive phase of Indian Ocean Subtropical Dipole. Warm temperatures and high precipitation in neighbouring countries were also observed two months prior to increased malaria incidence. This lagged association between regional climate and malaria incidence suggests that in areas at high risk for malaria, such as Limpopo, management plans should consider not only local climate patterns but those of neighbouring countries as well. These findings highlight the need to strengthen cross-border control of malaria to minimize its spread.

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.

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.

Humidity: A review and primer on atmospheric moisture and human health

Research examining associations between weather and human health frequently includes the effects of atmospheric humidity. A large number of humidity variables have been developed for numerous purposes, but little guidance is available to health researchers regarding appropriate variable selection. We examine a suite of commonly used humidity variables and summarize both the medical and biometeorological literature on associations between humidity and human health. As an example of the importance of humidity variable selection, we correlate numerous hourly humidity variables to daily respiratory syncytial virus isolates in Singapore from 1992 to 1994. Most water-vapor mass based variables (specific humidity, absolute humidity, mixing ratio, dewpoint temperature, vapor pressure) exhibit comparable correlations. Variables that include a thermal component (relative humidity, dewpoint depression, saturation vapor pressure) exhibit strong diurnality and seasonality. Humidity variable selection must be dictated by the underlying research question. Despite being the most commonly used humidity variable, relative humidity should be used sparingly and avoided in cases when the proximity to saturation is not medically relevant. Care must be taken in averaging certain humidity variables daily or seasonally to avoid statistical biasing associated with variables that are inherently diurnal through their relationship to temperature.