Modeling water inequality and water security: The role of water governance

Water Inequality, Water Security and Water Governance are fundamental parameters that affect the sustainable use of water resources. Through policy formulation and decision-making, Water Governance determines both Water Security and Water Inequality. Largely, where Water Inequality exists, Water Security is undermined through unsustainable water use practices that lead to pollution of water resources, conflicts, hoarding of water, and poor sanitation. Incidentally, the interconnectedness of Water Governance, Water Inequality and Water Security has not been investigated previously. This study modified the Gini coefficient and used a Logistics Growth of Water Resources Model (LGWR Model) to access Water Inequality and Water Security mathematically, and discussed the connected role of Water Governance. We tested the validity of both models by calculating the actual Water Inequality and Water Security of Ghana. We also discussed the implications of Water Inequality on Water Security and the overarching role of Water Governance. The results show that regional Water Inequality is widespread in some parts. The Volta region showed the highest Water Inequality (Gini index of 0.58), while the Central region showed the lowest (Gini index of 0.15). Water Security is moderately sustainable. The use of water resources is currently stress-free. It was estimated to maintain such status until 2132 ± 18 when Ghana will consume half of the current total water resources of 53.2 billion cubic meters. Effectively, Water Inequality is a threat to Water Security, results in poverty, under-development heightens tensions in water use, and causes instability. With proper Water Governance, Water Inequality can be eliminated through formulating and implementing approaches that engender equal allocation and sustainable use of water resources.

The effect of human settlement temperature and humidity on the growth rules of infected and recovered cases of COVID-19

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.

Mechanisms of recurrent outbreak of COVID-19: a model-based study

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.