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The purpose of this paper is to develop an active larval indices surveillance system and compare the outcomes of the implementation in primary care units (PCUs) at low and high risk of dengue.

The study design was conducted by implementing a community participation action research system in low and high dengue risk PCUs in Lansaka district, Nakhon Si Thammarat province, in the Southern Region of Thailand. There were five phases to the process including preparation of all stakeholders, situation assessment, development of the surveillance system, program implementation and evaluation. The system was developed in ten villages that were categorized as either low dengue risk PCUs (comprising six villages) or high dengue risk PCUs (four villages). A village was assigned as being at high or low dengue risk according to pre-determined criteria. The low dengue risk PCU assessments were conducted on a seven-step active larval indices surveillance system where PCU officials were additionally involved in coordinating, teaching, coaching and supporting the village health volunteers (VHVs) for dengue prevention activities. The high dengue risk PCUs, on the other hand, only followed a basic larval indices surveillance system with no follow-up support.

The outcomes of using intervention systems showed that the VHVs’ dengue knowledge and larval indices understanding in both PCUs increased significantly (p<0.01). Furthermore, the low dengue risk PCUs had a higher larval indices level than the high dengue risk PCU (p<0.01).

This study showed that the low dengue risk PCU followed an active larval indices surveillance system at the sub-district level which is appropriate for villages. This study also revealed that VHVs are needed to strengthen the capacity in terms of knowledge and skills of developing such a system to ensure reduced levels of dengue in the community.

This study evaluates the impact of government social protection interventions on households’ welfare in South Africa.

The study uses survey data comprising 393 observations and the multinomial logistic regression technique to analyse the effect of government interventions on households’ welfare. For robustness purposes, a negative binomial regression model is also estimated whose results corroborate the main results from the multinomial regression model.

The study’s findings show that government economic interventions through social protection significantly reduce the likelihood of a decrease in household income or consumption. COVID-19 grant/social relief of distress grant, unemployment insurance, tax relief and job protection and creation are all significant in sustaining household income and consumption.

The findings have policy implications for social development. Specifically, the findings support the use of government social protection as a safety net for low-income groups in South Africa.

The study presents preliminary evidence on the effectiveness of several measures used to ameliorate the COVID-19-induced recession within the South African context.

Quantitative and spatial-explicit flood risk information is of great importance for strengthening climate change adaptation and flood resilience. Shanghai is a coastal megacity at large estuary delta with rising flood risks. This study aims to quantify the overall economic-societal risks of storm flooding and their spatial patterns in Shanghai.

Based on multiple storm flood scenarios at different return periods, as well as fine-scale data sets including gridded GDP, gridded population and vector land-use, a probabilistic risk model incorporating geographic information system is used to assess the economic-societal risks of flooding and their spatial distributions.

Our results show that, from 1/200 to 1/5,000-year floods, the exposed assets will increase from USD 85.4bn to USD 657.6bn, and the direct economic losses will increase from USD 3.06bn to USD 52bn. The expected annual damage (EAD) of assets is around USD 84.36m. Hotpots of EAD are mainly distributed in the city center, the depressions along the upper Huangpu River in the southwest, the north coast of Hangzhou Bay, and the confluence of the Huangpu River and Yangtze River in the northeast. From 1/200 to 1/5,000-year floods, the exposed population will rise from 280 thousand to 2,420 thousand, and the estimated casualties will rise from 299 to 1,045. The expected annual casualties (EAC) are around 2.28. Hotspots of casualties are generally consistent with those of EAD.

In contrast to previous studies that focus on a single flood scenario or a particular type of flood exposure/risk in Shanghai, the findings contribute to an understanding of overall flood risks and their spatial patterns, which have significant implications for cost-benefit analysis of flood resilience strategies.