Coronavirus Disease 2019 In Countries Research Articles

Impact of Government Intervention in Response to Coronavirus Disease 2019

Coronavirus disease 2019 (COVID-19) led to the loss of lives and had serious social and economic effects. Countries implemented various quarantine policies to reduce the effects. The countries were divided into low- and high-risk groups based on the differences in quarantine policies and their levels of infection. Quarantine policies that significantly contributed to risk reduction were determined by analyzing 11 quarantine indicators for reducing the spread of COVID-19. The cross-tabulation and Chi-square tests were used to compare the quarantine policies by the groups. Multivariate logistic regression was used to determine the useful quarantine policies implemented by the low-risk group to verify quarantine policies for minimizing the negative effects. The analysis showed that the low- and medium-risk groups showed significant differences for 9 of the 11 indicators, and 4 of these differentiated the low- from the medium-risk group. Countries with strict quarantine policies related to workplace closure and staying at home were more likely to be included in the low-risk group. These policies had a significant impact in the low-risk countries and could contribute to reducing the spread and effects of COVID-19 in countries included in the high-risk group.

Does Fecal-Oral Transmission of SARS-CoV-2 Due to Low Sanitation Conditions Contribute to Low Mortality Rates From COVID-19.

Background: The novel coronavirus disease 2019 (COVID-19) is a global pandemic generated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The primary infection site is mucosal surfaces, mainly the lungs and the intestine, where epithelial cells can be infected. COVID-19 has spread throughout the world, causing millions of deaths and hundreds of millions of confirmed infections. Despite the global spread of SARS-CoV-2, there are extreme differences between countries in mortality rates and confirmed infections.Methods: Pearson correlations and a t-test were performed on data from 137 countries in order to test the correlation between number of deaths from diarrheal diseases (pre-COVID-19 pandemic data) as a marker for countries’ sanitation level, and the number of confirmed COVID-19 cases and deaths per million.Results: It was found that countries’ prevalence of confirmed COVID-19 cases and deaths per million are statistically correlated with their sanitation level.Conclusions: The hypothesis proposed in this article is that the low mortality rates from COVID-19 in countries where the level of sanitation is low are due to fecal-oral infection of the population by SARS-CoV-2, rather than infection of the respiratory system. This hypothesis is supported by the protective effect of the low sanitation level presented in this work and the fact that lung infection by SARS-CoV-2 can cause severe pathology, while infection in the intestine generally causes minor or no symptoms.

Accessing the syndemic of COVID-19 and malaria intervention in Africa

BackgroundThe pandemic of the coronavirus disease 2019 (COVID-19) has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. The aim of this study is to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.MethodsWe present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of reported COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (1) contact restriction and social distancing, and (2) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.ResultsWe conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R_0 and the duration of infection D_I) of COVID-19 in each country are estimated as follows: Ethiopia (R_0=1.57, D_I=5.32), Nigeria (R_0=2.18, D_I=6.58), Tanzania (R_0=2.47, D_I=6.01), and Zambia (R_0=2.12, D_I=6.96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020.ConclusionsBy quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

Socio-economic inequality in global incidence and mortality rates from coronavirus disease 2019: an ecological study.

aCoronavirus disease 2019 (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Epidemiological and clinical features of patients with COVID-19 have been examined but socio-economic aspects have been less studied. This study aimed to identify the role of the human development index (HDI) in the incidence and mortality rates of COVID-19 worldwide. Information on the incidence and mortality rates of COVID-19 was obtained from the Worldometer and data about the HDI 2019 were obtained from the World Bank database. Correlations between incidence, mortality rates and HDI parameters were assessed using linear regression. We calculated the concentration index to measure socio-economic inequality in COVID-19-related mortality and incidence. A linear regression analysis showed a direct significant correlation between the incidence and mortality rate of COVID-19 and HDI at the global level. The concentration index was positive for incidence rate (0.62) and mortality rate (0.69) of COVID-19, indicating the higher concentration of the rates among groups with high HDI. The high incidence and mortality rates of COVID-19 in countries with high and very high HDI are remarkable and should be the top priority for interventions by global health policy-makers. Health programmes should be provided to reduce the burden of this disease in regions with high incidence and mortality rates of COVID-19.

COVID-19 Pandemic in the World and its Relation to Human Development Index: A Global Study

: Coronaviruses are a large family and a subset of Coronaviridae that include common cold viruses and other severe diseases like severe acute respiratory syndrome (SARS-CoV), Middle East respiratory syndrome (MERS-CoV), and coronavirus disease 2019 (COVID-19). This is an ecological study based on statistics of the prevalence of coronavirus disease until 30 April 2020, based on the reports sent to the World Health Organization (WHO). This study investigates the distribution of the incidence and trend of the incidence rate of COVID-19 in countries, and its relation with the human development index (HDI) until 30 April 2020. The results showed that the most cases of coronavirus disease until the mentioned date were in the United States of America (1,003,947 cases), Spain (212,917 cases), Italy (203,591 cases), the United Kingdom (165,225 cases), and Germany (159,119 cases), in sequence. The results exhibited a significant positive correlation between the incidence of COVID-2019 and HDI in the world (r = 0.470, P < 0.0001).

National Preferred Interpersonal Distance Curbs the Spread of COVID-19: A Cross-Country Analysis.

National interpersonal distance preference is considered a cultural characteristic. Interpersonal distance is critical for the spread dynamics of coronavirus disease 2019 (COVID-19). COVID-19's spread trend shows various characteristics in different countries. We believe that 1 of the factors influencing this variation could be national interpersonal distance preference. We used regression analysis based on data of national interpersonal distance preferences (social, personal, and intimate) presented by Sorokowska et al. and COVID-19 rate of spread data for 40 different countries that were calculated using Our World in Data's data. National interpersonal distance preferences, with its 3 dimensions, significantly influence the rate of spread of COVID-19 in countries. Understanding the relation between national interpersonal distance preference and spread of COVID-19 might be very useful information in decision-making processes of individuals, societies, and governments to develop culturally well-suited counter-pandemic policies, strategies, and procedures during the COVID-19 pandemic or any epidemic or pandemic threats in the future, instead of standard fit-to-all strategies.

A Computational Model for Estimating the Progression of COVID-19 Cases in the US West and East Coast Population Regions.

The ongoing coronavirus disease 2019 (COVID-19) pandemic is of global concern and has recently emerged in the US. In this paper, we construct a stochastic variant of the SEIR model to estimate a quasi-worst-case scenario prediction of the COVID-19 outbreak in the US West and East Coast population regions by considering the different phases of response implemented by the US as well as transmission dynamics of COVID-19 in countries that were most affected. The model is then fitted to current data and implemented using Runge-Kutta methods. Our computation results predict that the number of new cases would peak around mid-April 2020 and begin to abate by July provided that appropriate COVID-19 measures are promptly implemented and followed, and that the number of cases of COVID-19 might be significantly mitigated by having greater numbers of functional testing kits available for screening. The model is also sensitive to assigned parameter values and reflects the importance of healthcare preparedness during pandemics.