Enabling the use of epidemiological, environmental and societal information by the public and stakeholders to anticipate, manage and mitigate emerging and re-emerging infectious diseases in Kandy, Sri Lanka.
Project title: Enabling the use of epidemiological, environmental and societal information by the public and stakeholders to anticipate, manage and mitigate emerging and re-emerging infectious diseases in Kandy, Sri Lanka.
Problem: In recent years, there is evidence of emergence of new diseases such as Chikungunya, Dengue, Leishmaniases, Japanese Encephalitis and Leptospirosis and re-emergence of diseases such as Malaria in the hill district of Kandy in Sri Lanka. Kandy is the largest metropolis in Sri Lanka after Colombo (1.4 million people in 1,917 sq. km) and it has been attracting increasing migration leading to rapid urbanization and land use change. Climate change is leading to both an alarming and unusual decline in rainfall and streamflow in the rivers through Kandy district (by about 10-20% over 3 decades) and a rise in temperature that is four times the global rate of warming. The hill region is particularly susceptible for the migration of disease risks to higher altitudes as temperature increases.
1. The overall goal is to promote multiple stakeholder participation in data generation, management, risk prediction and communication related to (re-) emerging infectious disease decisions.
2. To engage and mobilize multiple agencies, community groups and local government in a program to use information about epidemiology, environmental and societal factors to control and prevent emerging and re-emerging diseases.
3. To bring together expertise in the epidemiological and entomological, environmental, socio-economic and ICT fields to interrogate existing data to monitor epidemiological, land use, hydrological and climate and socio-economic information for interpreting hazard, exposure and vulnerability to EID at the sub-district and village scale in Kandy.
4. To develop a system of risk prediction for water-related vector-borne diseases based on the epidemiological, environmental and vulnerability information that already exists and can be routinely collected.
5. To enable stakeholder participation in assessing risks of disease and the social, environmental and vulnerability conditions that contribute to disease risk and for managing and mitigating these conditions.
Predicting Dengue Risk from Environmental, Entomological, and Societal Information in Kandy and Colombo
Problem: In recent years, dengue cases have been rising dramatically in the Central Province. Our work mainly focuses on obtaining dengue data from Kandy, Matale and Nuwara Eliya districts. It is useful to study the spread of dengue and the role of climate in Central Province. Dengue case data and entomology data is collected through MOH divisions for each district as well through the Regional Director of Health Services (RDHS) where daily, weekly and monthly data are being obtained.
We are collecting data on fine scale on Dengue cases and climate. We have collected data from 12 MOH divisions in Kandy district until 2018 April and obtained up to April 2018 data from the RDHS Matale. The table below summarizes the data that was accessed.
Table 1: Dengue case data accessed from MOH divisions in Central Province
Nuwara Eliya MOHs
Entomological data is crucial to analyze and identify the relationship with the temporal and spatial distribution of dengue incidences. The data collected includes the dengue vector indices, namely, container index, house index and breteau index for Ae. aegypti and Ae. albopictus and the different breeding sites of these vectors. Entomological investigations are being carried out at 10 sentinel sites covering 10 high dengue risk areas in the Kandy district .Monthly sampling data on vector density of these high dengue transmission MOH areas are available at the Regional Office of the Anti-malaria campaign in Kandy and at the relevant MOH Offices.
Table 2: Entomological data accessed from MOH divisions in Central Province
Where possible data shall be verified against the reports with health officials. We shall be cross checking meteorological data with those in neighboring stations and with satellite data for accuracy.
Exploratory Data Analysis
To look at the evolution of dengue cases over the record we plot a time series. the time series from 1997-2017 is shown in Figure 1 and the data is complete and consistent with neighboring districts and with aggregates of MOH level data. The highest dengue cases were observed in the year 2017.
Yearly cycle was used to identify relationship between dengue cases and climate variables for Kandy district from 2008-2017 (Figure 2).
According to the above graph the month with the highest dengue incidence are closely clustered at a maximum temperature 29-30 °C of and a minimum temperature of 23-24 °C. The peak month for rainfall is November and the peak month for maximum and minimum temperature is March.
We use scatter plots to reveal relationships between variables of interest such as dengue cases and climatic factors such as rainfall and temperature in an exploratory mode. We have monthly estimates for cases for Kandy and rainfall, minimum and maximum temperature estimated at Katugastota The scatter plots in Figure 3 show the relationship between dengue incidents and climatic properties (Monthly rainfall, Min/Max temperature) from 2008-2017 (Figure 2).
Correlation Analysis Composite Analysis
Virology and Immunology
Scientific literature is being carried out to assess changing serotypes in Sri Lanka and changes in Human immunity (Figure 4). We plan to identify the relationships between prevalence of different species of dengue vectors and dengue incidence by sub-district (MOH), district, region and season and to quantify human immunity to different strains of dengue.
We expect to compile data for dengue analysis at district, sub-district scale for dengue incidence, interpolated climate, entomology and its quality control, gap filling, interpolation, and exploratory data analysis. We shall review local history of human immunity and quality control. We will be carrying out vulnerability analysis using demographic data and look into history of control programs. We shall be implementing a model for vector abundance assuming there is a relationship between vector prevalence and dengue risk as literature has proven this in many regions. We shall implement this model for the target sub-districts. Such understanding will assist with dengue control and policy making in advance. It is necessary to collect data in areas where there is inadequate sufficient entomological data. Therefore we have purchased microscopes and other equipment for entomological surveillance. We will also undertake household larval surveys and weather observations.
Flyer- Climate Sensitivity of Dengue in the Central Province