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.


Data Collection

Dengue Cases

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

Kandy MOHs 2001-20102001-2010
Matale MOHs2004-2018April2004-2018April2004-2018April
Nuwara Eliya MOHs 2004-20082004-2008
Entomological Data

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

Nuwara Eliya
Quality Assessment

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
Time Series

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.

Figure 1: Dengue cases in Sri Lanka from 1997-2017
Yearly Cycle

Yearly cycle was used to identify relationship between dengue cases and climate variables for Kandy district from 2008-2017 (Figure 2).

Figure 2: Average number of cases of Dengue by month for Kandy is shown as the black line. The monthly average Rainfall is shown as the gray shading; the monthly average Minimum temperature is shown as the yellow bar; the monthly average Maximum temperature is shown as the orange bar from 2008 to 2017.

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.

Scatter Plots

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).

Figure 3: The monthly number of Dengue cases in Kandy district is plotted against Minimum temperature as yellow squares; Maximum temperature as orange triangle; Rainfall as blue dots from 2008 to 2017.
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.

Figure 4: The changing percentage of the DENV-1, DENV-2, DENV-3 and DENV-4 serotypes from 1989 to 2006 (Raheel et al., 2011).
Future Work

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