GIS based malaria information management system for urban malaria scheme in India

Quantifying climatic and socioeconomic drivers of urban malaria in Surat, India: a statistical spatiotemporal modelling study

BACKGROUND

Cities are becoming increasingly important habitats for mosquito vectors of disease. The pronounced heterogeneity of urban landscapes challenges our understanding of the effects of climate and socioeconomic factors on mosquito-borne disease dynamics at different spatiotemporal scales. Here, we quantify the impact of climatic and socioeconomic factors on urban malaria risk, using an extensive dataset in both space and time for reported Plasmodium falciparum cases in the city of Surat, northwest India.

METHODS

We analysed 10 years of monthly P falciparum cases resolved at three nested spatial resolutions (seven zones, 32 units, and 478 worker units) with a Bayesian hierarchical mixed model that incorporates the effects of population density, poverty, relative humidity, and temperature, in addition to random effects (structured and unstructured). To reduce dimensionality and avoid correlation of covariates, socioeconomic variables from survey data were summarised into main axes of variation using principal component analysis. With model selection, we identified the main drivers of spatiotemporal variation in malaria incidence rates at each of the three spatial resolutions. We also compared observations to model-fitted cases by quantifying the percentage of predictions within five discrete levels of malaria risk.

FINDINGS

The spatial variation of urban malaria cases was stationary over time, whereby locations with high and low yearly cases remained largely consistent across years. Local socioeconomic variation could be summarised with three principal components accounting for approximately 80% of the variance. The model that incorporated local temperature and relative humidity together with two of these principal components, largely representing population density and poverty, best explained monthly malaria patterns in models formulated at the three different spatial scales. As model resolution increased, the effect size of humidity decreased, whereas those of temperature and the principal component associated with population density increased. Model predictions accurately captured aggregated total monthly cases for the city; in space-time, they more closely matched observations at the intermediate scale, with around 57% of units estimated to fall in the observed category on average across years. The mean absolute error was lower at the intermediate level, showing that this is the best aggregation level to predict the space-time dynamics of malaria incidence rates across the city with the selected model.

INTERPRETATION

This statistical modelling framework provides a basis for development of a climate-driven early warning system for urban malaria for the units of Surat, including spatially explicit prediction of malaria risk several weeks to months in advance. Results indicate environmental and socioeconomic covariates for which further measurement at high resolution should lead to model improvement. Advanced warning combined with local surveillance and knowledge of disease hotspots within the city could inform targeted intervention as part of urban malaria elimination efforts.

FUNDING

US National Institutes of Health.

Receptivity to malaria: meaning and measurement

"Receptivity" to malaria is a construct developed during the Global Malaria Eradication Programme (GMEP) era. It has been defined in varied ways and no consistent, quantitative definition has emerged over the intervening decades. Despite the lack of consistency in defining this construct, the idea that some areas are more likely to sustain malaria transmission than others has remained important in decision-making in malaria control, planning for malaria elimination and guiding activities during the prevention of re-establishment (POR) period. This manuscript examines current advances in methods of measurement. In the context of a decades long decline in global malaria transmission and an increasing number of countries seeking to eliminate malaria, understanding and measuring malaria receptivity has acquired new relevance.

Surveillance considerations for malaria elimination

Constant malaria monitoring and surveillance systems have been highlighted as critical for malaria elimination. The absence of robust monitoring and surveillance systems able to respond to outbreaks in a timely manner undeniably contributed to the failure of the last global attempt to eradicate malaria. Today, technological advances could allow for rapid detection of focal outbreaks and improved deployment of diagnostic and treatment supplies to areas needing support. However, optimizing diffusion activities (e.g., distributing vector controls and medicines, as well as deploying behaviour change campaigns) requires networks of diverse scholars to monitor, learn, and evaluate data and multiple organizations to coordinate their intervention activities. Surveillance systems that can gather, store and process information, from communities to national levels, in a centralized, widely accessible system will allow tailoring of surveillance and intervention efforts. Different systems and, thus reactions, will be effective in different endemic, geographical or socio-cultural contexts. Investing in carefully designed monitoring technologies, built for a multiple-acter, dynamic system, will help to improve malaria elimination efforts by improving the coordination, timing, coverage, and deployment of malaria technologies.

Open-Source web-based Geographical Information System for health exposure assessment

This paper presents the design and development of an open source web-based Geographical Information System allowing users to visualise, customise and interact with spatial data within their web browser. The developed application shows that by using solely Open Source software it was possible to develop a customisable web based GIS application that provides functions necessary to convey health and environmental data to experts and non-experts alike without the requirement of proprietary software.

Uncertainty in mapping malaria epidemiology: implications for control

Malaria is a location-specific, dynamic infectious disease transmitted by mosquitoes to humans and is influenced by environmental, vector, parasite, and host factors. The principal purposes of malarial epidemiology are 1) to describe the malarial distribution in space and time along with the physical, biologic, and social etiologic factors and 2) to guide control objectives for either modeling impact or measuring progress of control tactics. Mapping malaria and many of its causative factors has been achieved on many different levels from global distribution to biologic quantitative trait localization in humans, parasites, and mosquitoes. Despite these important achievements, a large degree of uncertainty still exists on the annual burden of malarial cases. Accurate, sensitive detection and treatment of asymptomatic reservoirs important to infectious transmission are additional components necessary for future control measures. Presently spurred by the leadership and funding of Bill and Melinda Gates, the malarial community is developing and implementing plans for elimination of malaria. The challenge for malariologists is to digitally integrate and map epidemiologic factors and intervention measures in space and time to target effective, sustainable control alongside research efforts.

Use of Google Earth to strengthen public health capacity and facilitate management of vector-borne diseases in resource-poor environments

OBJECTIVE

Novel, inexpensive solutions are needed for improved management of vector-borne and other diseases in resource-poor environments. Emerging free software providing access to satellite imagery and simple editing tools (e.g. Google Earth) complement existing geographic information system (GIS) software and provide new opportunities for: (i) strengthening overall public health capacity through development of information for city infrastructures; and (ii) display of public health data directly on an image of the physical environment.

METHODS

We used freely accessible satellite imagery and a set of feature-making tools included in the software (allowing for production of polygons, lines and points) to generate information for city infrastructure and to display disease data in a dengue decision support system (DDSS) framework.

FINDINGS

Two cities in Mexico (Chetumal and Merida) were used to demonstrate that a basic representation of city infrastructure useful as a spatial backbone in a DDSS can be rapidly developed at minimal cost. Data layers generated included labelled polygons representing city blocks, lines representing streets, and points showing the locations of schools and health clinics. City blocks were colour-coded to show presence of dengue cases. The data layers were successfully imported in a format known as shapefile into a GIS software.

CONCLUSION

The combination of Google Earth and free GIS software (e.g. HealthMapper, developed by WHO, and SIGEpi, developed by PAHO) has tremendous potential to strengthen overall public health capacity and facilitate decision support system approaches to prevention and control of vector-borne diseases in resource-poor environments.