How do we improve the quality of representation in new democracies? This paper studies candidate selection by party leaders and asks whether poor information about public preferences can lead elite choices to diverge from mass opinion. Working with a political party in Nepal, we show that while elites value voter preferences, these preferences only explain onethird of elite candidate selection. Next, we embed an experiment in actual candidate selection deliberations for this party and find that party leaders not only select different candidates when polling data are presented to them, but that their updated decisions also improve the party's vote share. By opening the black-box of candidate selection, this paper demonstrates that closing the information gap between elites and voters has the power to improve the quality of representation.

The coronavirus disease 19 (COVID-19), the biggest health problem at present, does not have uniform transmission and severity among the countries and communities therein. Knowledge of community vulnerability to the disease would facilitate interventions aimed at transmission control by the efficient deployment of available limited resources. Therefore, we assessed spatial variations and heterogeneity of disease vulnerability among the population in 753 municipal units of Nepal. We collected geospatial indicators representing the domain of socioeconomic inequalities, population dynamics, heterogeneity in the accessibility and the information related to an underlying health condition which potentially affect the severity of COVID-19 transmission. Those indicators were assembled to create three vulnerability indices using Geographic Information System (GIS); Social Vulnerability Index (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two- Social and Epidemiological Vulnerability Index (SEVI). The indicators were scaled to a common measurement scale and spatially overlaid via equally weighted arithmetic mean. The indices were classified into five level of risk and the municipal units and the population within vulnerabilities classes were quantified and visualized in the map. The index output indicated high vulnerability to epidemics in megacities like Kathmandu, Pokhara, Bharatpur, etc.; developing cities especially in the Province No 2; and, municipal units of Karnali and Sudoorpashchim provinces. Additionally, some other municipalities such as Dhulikhel, Beshishahar, Tansen etc. which have a higher prevalence of pulmonary and cardiovascular disorders are highly vulnerable. The SVI indicated that 174 municipal units and 41.5% population is highly vulnerable. The EVI identified 55 municipal units and 40.7% of the total population of the country highly vulnerable to COVID-19. The SEVI accounted that disease vulnerability is high in 105 municipal units and 40% population of Nepal. The vulnerability indices created are means for different tiers of the existing government in federal system of Nepal for prioritization and improved planning for disease intervention especially in highly vulnerable municipal units where the COVID-19 transmission could have high severity.

The coronavirus disease 2019 (COVID-19), the biggest health problem at present, doesn’t have uniform transmission and severity among the countries and communities therein. Knowledge of community vulnerability to the disease would facilitate interventions aimed at transmission control by efficient deployment of available limited resources. Therefore, we assessed spatial variations and heterogeneity of disease vulnerability among the population in 753 municipal units of Nepal. We collected geospatial indicators representing the domain of socioeconomic inequalities, population dynamics, heterogeneity in accessibility and the information related to underlying health condition which potentially affect the severity of COVID-19 transmission. Those indicators were scaled to a common measurement scale and spatially overlaid via equally weighted arithmetic mean and then assembled to create three vulnerability indices using Geographic Information System; Social Vulnerability Index (SVI), Epidemiological Vulnerability Index (EVI) and a composite of the two- Social and Epidemiological Vulnerability Index (SEVI). The indices were classified into five level of vulnerability and the municipal units and the population within vulnerability classes were quantified and visualized in the map. The index output indicated high vulnerability to epidemics in metropolitan cities like Kathmandu, Pokhara, Bharatpur, etc.; developing cities especially in the Province No 2; and, municipal units of Karnali and Sudoorpashchim provinces. Additionally, some other municipalities such as Dhulikhel, Beshishahar, Tansen etc. which have a higher prevalence of pulmonary and cardiovascular disorders are highly vulnerable. The SVI indicated that 174 municipal units and 41.5% population is highly vulnerable. The EVI identified 55 municipal units and 40.7% of the total population of the country highly vulnerable to COVID-19. The SEVI accounted that disease vulnerability is high in 105 municipal units and 40% population of Nepal. The vulnerability indices created are means for different tiers of the existing government in federal system of Nepal for prioritization and improved planning for disease intervention especially in highly vulnerable municipal units where the COVID-19 transmission could have high severity.

Nepal has been strongly influenced by the COVID-19 pandemic and struggling to contain it with multiple interventions. We assessed the spatiotemporal dynamics of COVID-19 in the context of various restrictions imposed to contain the disease transmission by employing prospective spatiotemporal analysis with SaTScan statistics. We explored active and emerging disease clusters using the prospective space-time scanning with the Discrete Poisson model for two time periods using COVID-19 cases reported to the Ministry of Health and Population (MoHP), Government of Nepal during 23 January – 21 July, and 23 January – 29 November 2020 taking the cutoff date of 21 July (end date of nationwide lockdown). The results revealed that COVID-19 dynamics in the early transmission stage were slower and confined to a few districts. However, since the third week of April, transmission spread rapidly across the districts of Madhesh and Sudurpaschim Provinces. Despite nationwide lockdown, nine statistically significant active and emerging clusters were detected between 23 January and 21 July 2020, whereas seven emerging clusters were observed for an extended period to 29 November. After lifting the nationwide lockdown, COVID-19 clusters developed had a many-fold higher relative risk than during the lockdown period. The most likely cluster was located in the capital city, the Kathmandu valley, making it the highest-risk active cluster since August. Movement restriction appears to be the most effective non-pharmaceutical intervention against the COVID-19 in countries with limited health care facilities. Our findings could be valuable to the health authorities within Nepal and beyond to better allocate resources and improve interventions on the pandemic for containing it efficiently.