Index-based tools for livelihood security and resilience assessment (LiSeRA) in lower Mekong Basin.

This paper presents a framework and toolkit for assessment of multi-hazard livelihood security and resilience in the Lower Mekong Basin (LMB) communities. The LMB is a subsidiary region of the Mekong region in South East Asia, and is frequently exposed to hydrometeorological hazards and anthropogenic stressors that expose and directly affect the livelihoods of more than sixty-five million people living in the region. The main purpose of the study is to support decision-making and risk management planning through integration of the concepts of livelihood security and resilience into a holistic framework, and subsequently developing an index-based toolkit for conducting assessments. Firstly, dimensions, sub-dimensions and indicators for measurement of livelihood security and resilience in the LMB were identified through comprehensive literature review and expert consultation. Then, several local workshops were conducted with various stakeholders (researchers, government officials, community people) in the LMB region to validate the indicators and generate weightages. The indicators were then arranged in a matriculated form, and the weightages were used to generate the algorithm for computing the quantitative outputs of livelihood security and resilience in study area. An Excel toolkit and a 'R' programming package were developed using the algorithm for visualization of the assessment outcomes. The proposed framework and toolkit are expected to assist researchers, government officials and development professionals in generating robust resilience assessment indices for risk informed decision-making and planning. Brief outline of the method •Livelihood security and resilience concepts were integrated to generate a holistic assessment framework and an indicator library.•Weightages for indicators were generated using the Analytical Hierarchical Process (AHP) through consultation with relevant stakeholders.•The indicator library was developed into an algorithm-based Excel and 'R' programming toolkit that provides quantitative assessment outputs.

Significance of geographical factors to the COVID-19 outbreak in India.

Recently, the large outbreak of COVID-19 cases all over the world has whacked India with about 30,000 confirmed cases within the first 3 months of transmission. The present study used long-term climatic records of air temperature (T), rainfall (R), actual evapotranspiration (AET), solar radiation (SR), specific humidity (SH), wind speed (WS) with topographic altitude (E) and population density (PD) at the regional level to investigate the spatial association with the number of COVID-19 infections (NI). Bivariate analysis failed to find any significant relation (except SR) with the number of infected cases within 36 provinces in India. Variable Importance of Projection (VIP) through Partial Least Square (PLS) technique signified higher importance of SR, T, R and AET. However, generalized additive model fitted with the log-transformed value of input variables and applying spline smoothening to PD and E, significantly found high accuracy of prediction (R 2 = 0.89), and thus well-explained complex heterogeneity among the association of regional parameters with COVID-19 cases in India. Our study suggests that comparatively hot and dry regions in lower altitude of the Indian territory are more prone to the infection by COVID-19 transmission.