This study investigated the impact of COVID-19 on the insurance industry by studying the case of Ghana from March to June 2020. With a parallel comparison to previous pandemics such as SARS-CoV, H1N1 and MERS, we developed outlines for simulating the impact of the pandemic on the insurance industry. The study used qualitative and quantitative interviews to estimate the impact of the pandemic. Presently, the trend is an economic recession with decreasing profits but increasing claims. Due to the cancellation of travels, events and other economic losses, the Ghanaian insurance industry witnessed a loss currently estimated at GH Ȼ112 million. Our comparison and forecast predicts a normalization of economic indicators from January 2021. In the meantime, while the pandemic persists, insurers should adapt to working from remote locations, train and equip staff to work under social distancing regulations, enhance cybersecurity protocols and simplify claims/premium processing using e-payment channels. It will require the collaboration of the Ghana Ministry of Health, Banking Sector, Police Department, Customs Excise and Preventive Service, other relevant Ministries and the international community to bring the pandemic to a stop.
Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.
This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = − 0.21; p = 0.27) and temperature (r = −0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.
The Yangtze River Economic Delta (YRED) faces inequality in water use in large proportions due to rapid industrialization. This study adopted the Gini coefficient and Global Moran’s index to calculate inequality, its spatial spread and water use efficiency of cities in the YRED and categorized them into types based on the spatial spread of inequality. In general, inequality is reducing, but water use efficiency is poor. Inequality was rated 0–1; zero being the highest equality while 1 indicates the highest inequality. There is relatively high inequality (0.4–0.5) in Shanghai, Suzhou and Hefei. Most cities (20), however, showed equality (below 0.2). Nine (9) cities showed relative equality (0.2–0.3), while Wuxi, Bengbu and Zhenjiang were neutral (0.3–0.4). No city scored above 0.5. Water use efficiency in the majority of cities was poor. Only 11 out of 35 cities scored more than 50% efficiency. Poor irrigation, income and industrial water demand are the factors driving inefficiency and inequality. The categorization of cities into groups produced nine city types according to the spatial disposition of inequality. A combined effort to formulate policies targeting improved water use efficiency, reduced industrial consumption and improved irrigation, tailored towards the specific situation of each city type, would eliminate inequality.
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