In 2017, a dengue epidemic of unexpected magnitude occurred in Sri Lanka. A total of 186,101 suspected cases and 440 dengue-related deaths occurred. We conducted a comprehensive analysis of this epidemic by comparing national surveillance data for 2017 with data from the preceding 5 years. In all Sri Lanka districts, dengue incidence in 2017 increased significantly over incidence during the previous 5 years. Older schoolchildren and young adults were more clinically symptomatic than those at extremes of age. Limited virologic surveillance showed the dominant circulating variant was dengue virus type 2 cosmopolitan genotype in the most affected district. One quarter of total annual cases were reported 5 weeks after the southwest monsoon started. Changes in vector abundance were not predictive of the increased incidence. Direct government expenditures on dengue control activities in 2017 were US $12.7 million. The lessons learned from this outbreak are useful for other tropical nations facing increasing dengue incidence.

Dengue is one of the major public health problems in Sri Lanka. Its outbreak pattern depends on a multitude of drivers, including human mobility. Here we evaluate the impact of COVID–19 related mobility restriction (lockdown) on the risk of dengue in Sri Lanka. Two-stage hierarchical models were fitted using an interrupted time-series design based on the notified dengue cases, January 2015 to July 2020. In the first stage model, the district level impact was estimated using quasi-Poisson regression models while accounting for temporal trends. Estimates were pooled at zonal and national levels in the second stage model using meta-analysis. The influence of the extended period of school closure on dengue in children in the western province was compared to adults. Statistically significant and homogeneous reduction of dengue risk was observed at all levels during the lockdown. Overall an 88% reduction in risk (RR 0.12; 95% CI from 0.08 to 0.17) was observed at the national level. The highest impact was observed among children aged less than 19 years showing a 92% reduction (RR 0.8; 95% CI from 0.03 to 0.25). We observed higher impact in the dry zone having 91% reduction (RR 0.09; 95% CI from 0.05 to 0.15) compared to wet zone showing 83% reduction (RR 0.17; 95% CI from 0.09 to 0.30). There was no indication that the overall health-seeking behaviour for dengue had a substantial influence on these estimates. This study offers a broad understanding of the change in risk of dengue during the COVID–19 pandemic and associated mobility restrictions in Sri Lanka. The analysis using the mobility restrictions as a natural experiment suggests mobility patterns to be a very important driver of dengue transmission. Every year about 100 million symptomatic dengue cases occur with the highest-burden reported in Southeast Asia followed by Latin America. The burden is increasing and considered driven by population densities, human mobility, vector proliferation, and climate variability. Evaluation of the impact of human mobility on dengue transmission is methodologically challenging, but an important aspect in public health research. The ongoing COVID-19 pandemic has resulted in dramatic reductions in human mobility due to social distancing measures. These population-level interventions in most countries have changed the exposure patterns to Aedes vectors and the spread of the dengue virus. The mobility restrictions due to the COVID-19 response in Sri Lanka provided a natural experiment to understand the importance of human mobility on dengue in an endemic setting. Using interrupted time series design, we evaluated the impact of mobility restriction on dengue transmission in all 25 administrative districts, climate zones, and age groups in Sri Lanka. The results of this empirical study provided evidence of an overall statistically significant reduction of dengue during the period of mobility restriction in the country, which appears not to be related to changes in health seeking behaviour. Our study provides a methodological framework to quantify the impact of lockdown interventions on dengue transmission and its heterogeneous nature at different geographical, climatic, and socio-demographic levels within and between countries.

Dengue is the major public health burden in Sri Lanka. Kalutara is one of the highly affected districts. Understanding the drivers of dengue is vital in controlling and preventing the disease spread. This study focuses on quantifying the influence of weather variability on dengue incidence over 10 Medical Officer of Health (MOH) divisions of Kalutara district. Weekly weather variables and data on dengue notifications, measured at 10 MOH divisions in Kalutara from 2009 to 2013, were retrieved and analysed. Distributed lag non-linear model and hierarchical-analysis was used to estimate division specific and overall relationships between weather and dengue. We incorporated lag times up to 12 weeks and evaluated models based on the Akaike Information Criterion. Consistent exposure-response patterns between different geographical locations were observed for rainfall, showing increasing relative risk of dengue with increasing rainfall from 50 mm per week. The strongest association with dengue risk centred around 6 to 10 weeks following rainfalls of more than 300 mm per week. With increasing temperature, the overall relative risk of dengue increased steadily starting from a lag of 4 weeks. We found similarly a strong link between the Oceanic Niño Index to weather patterns in the district in Sri Lanka and to dengue at a longer latency time confirming these relationships. Part of the influences of rainfall and temperature can be seen as mediator in the causal pathway of the Ocean Niño Index, which may allow a longer lead time for early warning signals. Our findings describe a strong association between weather, El Niño-Southern Oscillation and dengue in Sri Lanka.

Dengue has become a major public health problem in Sri Lanka with a considerable economic burden. As a response, in June, 2014, the Ministry of Health initiated a proactive vector control programme in partnership with military and police forces, known as the Civil-Military Cooperation (CIMIC) programme, that was targeted at high-risk Medical Officer of Health (MOH) divisions in the country. Evaluating the effectiveness and cost-effectiveness of population-level interventions is essential to guide public health planning and resource allocation decisions, particularly in resource-limited health-care settings. Using an interrupted time series design with a non-linear extension, we evaluated the impact of vector control interventions from June 22, 2014, to Dec 29, 2016, in Panadura, a high-risk MOH division in Western Province, Sri Lanka. We used dengue notification and larval survey data to estimate the reduction in Breteau index and dengue incidence before and after the intervention using two separate models, adjusting for time-varying confounding variables (ie, rainfall, temperature, and the Oceanic Niño Index). We also assessed the cost and cost-effectiveness of the CIMIC programme from the perspective of the National Dengue Control Unit under the scenarios of different levels of hospitalisation of dengue cases (low [25%], medium [50%], and high [75%]) in terms of cost per disability-adjusted life-year averted (DALY). Vector control interventions had a significant impact on combined Breteau index (relative risk reduction 0·43, 95% CI 0·26 to 0·70) and on dengue incidence (0·43, 0·28 to 0·67), the latter becoming prominent 2 months after the intervention onset. The mean number of averted dengue cases was estimated at 2192 (95% CI 1741 to 2643), and the total cost of the CIMIC programme at 2016 US$271 615. Personnel costs accounted for about 89% of the total cost. In the base-case scenario of moderate level of hospitalisation, the CIMIC programme was cost-saving with a probability of 70% under both the lowest ($453) and highest ($1686) cost-effectiveness thresholds, resulting in a net saving of $20 247 (95% CI -57 266 to 97 790) and averting 176 DALYs (133 to 226), leading to a cost of -$98 (-497 to 395) per DALY averted. This was also the case for the scenario with high hospitalisation levels (cost per DALY averted -$512, 95% CI -872 to -115) but with a higher probability of 99%. In the scenario with low hospitalisation levels (cost per DALY averted $690, 143 to 1379), although the CIMIC programme was cost-ineffective at the lowest threshold with a probability of 77%, it was cost-effective at the highest threshold with a probability of 99%. This study suggests that communities affected by dengue can benefit from investments in vector control if interventions are implemented rigorously and coordinated well across sectors. By doing so, it is possible to reduce the disease and economic burden of dengue in endemic settings. None.

Background: Dengue is a major public health problem in Sri Lanka. Aedes vector surveillance and monitoring of larval indices are routine, long-established public health practices in the country. However, the association between Aedes larval indices and dengue incidence is poorly understood. It is crucial to evaluate lagged effects and threshold values of Aedes larval indices to set pragmatic targets for sustainable vector control interventions. Methods: Monthly Aedes larval indices and dengue cases in all 10 Medical Officer of Health (MOH) divisions in Kalutara district were obtained from 2010 to 2019. Using a novel statistical approach, a distributed lag non-linear model and a two-staged hierarchical meta-analysis, we estimated the overall non-linear and delayed effects of the Premise Index (PI), Breteau Index (BI) and Container Index (CI) on dengue incidence in Kalutara district. A set of MOH division-specific variables were evaluated within the same meta-analytical framework to determine their moderator effects on dengue risk. Using generalized additive models, we assessed the utility of Aedes larval indices in predicting dengue incidence. Results: We found that all three larval indices were associated with dengue risk at a lag of 1 to 2 months. The relationship between PI and dengue was homogeneous across MOH divisions, whereas that with BI and CI was heterogeneous. The threshold values of BI, PI and CI associated with dengue risk were 2, 15 and 45, respectively. All three indices showed a low to moderate accuracy in predicting dengue risk in Kalutara district. Conclusions: This study showed the potential of vector surveillance information in Kalutara district in developing a threshold-based, location-specific early warning system with a lead time of 2 months. The estimated thresholds are nonetheless time-bound and may not be universally applicable. Whenever longitudinal vector surveillance data areavailable, the methodological framework we propose here can be used to estimate location-specific Aedes larval index thresholds in any other dengue-endemic setting. Graphical

A thermo-stable clay device to maintain in vitro Leishmania cultures in tropical countries within the optimal temperature range of 22 to 27 degrees C was developed. This being simple and economical could immensely benefit diagnosis and research on leishmaniasis in these countries.