Relative roles of weather variables and change in human population in malaria: comparison over different states of India

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R²: 0.944) and Tripura (RMSE: 0.0451; R²: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.

Weather integrated malaria prediction system using Bayesian structural time series model for northeast states of India

Malaria is an endemic disease in India and targeted to eliminate by the year 2030. The present study is aimed at understanding the epidemiological patterns of malaria transmission dynamics in Assam and Arunachal Pradesh followed by the development of a malaria prediction model using monthly climate factors. A total of 144,055 cases in Assam during 2011-2018 and 42,970 cases in Arunachal Pradesh were reported during the 2011-2019 period observed, and Plasmodium falciparum (74.5%) was the most predominant parasite in Assam, whereas Plasmodium vivax (66%) in Arunachal Pradesh. Malaria transmission showed a strong seasonal variation where most of the cases were reported during the monsoon period (Assam, 51.9%, and Arunachal Pradesh, 53.6%). Similarly, the malaria incidence was highest in the male population in both states (Asam, 55.75%, and Arunachal Pradesh, 51.43%), and the disease risk is also higher among the > 15 years age group (Assam, 61.7%, and Arunachal Pradesh, 67.9%). To predict the malaria incidence, Bayesian structural time series (BSTS) and Seasonal Auto-Regressive Integrated Moving Average with eXogenous factors (SARIMAX) models were implemented. A statistically significant association between malaria cases and climate variables was observed. The most influencing climate factors are found to be maximum and mean temperature with a 6-month lag, and it showed a negative association with malaria incidence. The BSTS model has shown superior performance on the optimal auto-correlated dataset (OAD) which contains auto-correlated malaria cases, cross-correlated climate variables besides malaria cases in both Assam (RMSE, 0.106; MAE, 0.089; and SMAPE, 19.2%) and Arunachal Pradesh (RMSE, 0.128; MAE, 0.122; and SMAPE, 22.6%) than the SARIMAX model. The findings suggest that the predictive performance of the BSTS model is outperformed, and it may be helpful for ongoing intervention strategies by governmental and nongovernmental agencies in the northeast region to combat the disease effectively.

Ligand Decorated Primaquine Loaded Nanocarriers for Liver Targeting for Triggered Anti-Malarial Activity

OBJECTIVE

The aim of the current research is to formulate a nano delivery system for effective delivery of primaquine for liver targeting to achieve the potential anti-malarial activity. Another objective of current development is to formulate a lactobionic acid conjugated polyphosphazene based nano delivery of primaquine for liver targeting to distinguish anti-malarial activity.

METHOD

The particle size, entrapment efficiency, in-vitro drug release pattern, hepatotoxicity, MTT assay, erythrocyte toxicity assay, histopathology study, HepG2 cell uptake study, anti-- malarial study, and organ-distribution was also carried out to estimate the activity and potential features of a nanoparticle system.

RESULTS

The results obtained from the above analysis justify the efficiency and effectiveness of the system. The NMR studies confirm the conjugation pattern and the TEM represents the spherical morphological features of nanoparticles. The controlled release pattern from the in-vitro release study was observed and found to be 73.25% of drug release in 20 hrs and in the nano-size range (61.6± 1.56 nm) by particle size analysis.SGOT level, SGPT, ALP, and Parasitemia level of optimized drug-loaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles (FF) was found to lie in the safe range, showing that the formulation is non-toxic to the liver. Primaquine drug-loaded PEGylated lactobionic acid conjugated polyphosphazene polymeric nanoparticles showed higher cell uptake on HepG2 cell lines as compared to the drug-loaded in PEGylated polyphosphazene polymeric nanoparticles and plain drug.Percentage cell viability of drugloaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles was decreased by enhancing the concentration of prepared nanoparticle system accessed by MTT assay.

CONCLUSION

From the studies, it can be concluded that the optimized formulation of drug-loaded PEGylated lactobionic acid conjugated polyphosphazene derivatized nanoparticles showed high liver targeting, least toxicity to the liver, controlled release of the drug, higher anti-malarial activity against hepatocytes at a low dose, more effectiveness, and can be treated as a potential candidate for anti-malarial therapy.

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.

[Climate-sensitive diseases in Brazil and the world: systematic reviewEnfermedades sensibles al clima en Brasil y el mundo: revisión sistemática]

OBJECTIVE

To survey the literature regarding climate-sensitive diseases (CSD) and the impacts of climate changes on health.

METHOD

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The Lilacs, SciELO, Scopus, and PubMed databases were searched in July 2017 without temporal restrictions for articles published in in Portuguese, English and Spanish. The following search strategy was used in all databases: (climate) AND (disease) AND (sensitive).

RESULTS

The systematic review included 106 articles, most of which focused on dengue, malaria, and respiratory and cardiovascular diseases. The most commonly studied climate variables were temperature and precipitation. The studies revealed a relationship between the incidence of certain diseases, especially cardiovascular and respiratory diseases, dengue, malaria, and arboviral diseases, and climate conditions in different regions of the world. This relationship was analyzed considering both past data on the incidence of diseases and climate variables and projections regarding the future incidence of diseases according to expected climate variations. A greater number of studies was performed by authors originating from developed countries. The world regions most often studied were China, the United States, Australia, and Brazil.

CONCLUSIONS

Despite the increase in the number of published articles on this theme, a greater number of climate and environmental variables must be studied, with expansion of studies to additional regions in the world.

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.

Biology, distribution and control of Anopheles (Cellia) minimus in the context of malaria transmission in northeastern India

Among six dominant mosquito vector species involved in malaria transmission in India, Anopheles minimus is a major species in northeast India and held responsible for focal disease outbreaks characterized by high-rise of Plasmodium falciparum infections and attributable death cases. It has been now genetically characterized that among the three-member species of the Minimus Complex spread in Asia, An. minimus (former species A) is prevalent in India including northeastern states and east-central state of Odisha. It is recorded in all seasons and accounts for perennial transmission evidenced by records of sporozoite infections. This species is highly anthropophilic, and largely endophilic and endophagic, recorded breeding throughout the year in slow flowing seepage water streams. The populations of An. minimus in India are reported to be highly diverse indicating population expansion with obvious implications for judicious application of vector control interventions. Given the rapid ecological changes due to deforestation, population migration and expansion and developmental activities, there is scope for further research on the existence of potential additional sibling species within the An. minimus complex and bionomics studies on a large geographical scale for species sanitation. For control of vector populations, DDT continues to be applied on account of retaining susceptibility status even after decades of residual spraying. Anopheles minimus is a highly adaptive species and requires continuous and sustained efforts for its effective control to check transmission and spread of drug-resistant malaria. Anopheles minimus populations are reportedly diminishing in northeastern India whereas it has staged comeback in east-central State of Odisha after decades of disappearance with its eco-biological characteristics intact. It is the high time to siege the opportunity for strengthening interventions against this species for its population diminution to sub-optimal levels for reducing transmission in achieving malaria elimination by target date of 2030.

Towards seasonal forecasting of malaria in India

Background

Malaria presents public health challenge despite extensive intervention campaigns. A 30-year hindcast of the climatic suitability for malaria transmission in India is presented, using meteorological variables from a state of the art seasonal forecast model to drive a process-based, dynamic disease model.

Methods

The spatial distribution and seasonal cycles of temperature and precipitation from the forecast model are compared to three observationally-based meteorological datasets. These time series are then used to drive the disease model, producing a simulated forecast of malaria and three synthetic malaria time series that are qualitatively compared to contemporary and pre-intervention malaria estimates. The area under the Relative Operator Characteristic (ROC) curve is calculated as a quantitative metric of forecast skill, comparing the forecast to the meteorologically-driven synthetic malaria time series.

Results and discussion

The forecast shows probabilistic skill in predicting the spatial distribution of Plasmodium falciparum incidence when compared to the simulated meteorologically-driven malaria time series, particularly where modelled incidence shows high seasonal and interannual variability such as in Orissa, West Bengal, and Jharkhand (North-east India), and Gujarat, Rajastan, Madhya Pradesh and Maharashtra (North-west India). Focusing on these two regions, the malaria forecast is able to distinguish between years of “high”, “above average” and “low” malaria incidence in the peak malaria transmission seasons, with more than 70% sensitivity and a statistically significant area under the ROC curve. These results are encouraging given that the three month forecast lead time used is well in excess of the target for early warning systems adopted by the World Health Organization. This approach could form the basis of an operational system to identify the probability of regional malaria epidemics, allowing advanced and targeted allocation of resources for combatting malaria in India.