Environmental and climatic impact on the infection and mortality of SARS-CoV-2 in Peru

Abstract Objectives The role of the environment and climate in the transmission and case fatality rates of SARS-CoV-2 is still being investigated a year into the pandemic. Elevation and air quality are believed to be significant factors in the development of the pandemic, but the influence of additional environmental factors remains unclear. Methods We explored the relationship between the cumulative number of infections and mortality cases with climate (temperature, precipitation, solar radiation, water vapor pressure, wind), environmental data (elevation, normalized difference vegetation index or NDVI, particulate matter at 2.5 μm or PM2.5 and NO2 concentration), and population density in Peru. We use confirmed cases of infection from 1,287 districts and mortality in 479 districts, we used Spearman’s correlations to assess the bivariate correlation between environmental and climatic factors with cumulative infection cases, cumulative mortality and case-fatality rate. We explored district cases within the ecozones of coast, sierra, high montane forest and lowland rainforest. Results Multiple linear regression models indicate elevation, mean solar radiation, air quality, population density and green vegetation cover, as a socioeconomic proxy, are influential factors in the distribution of infection and mortality of SARS-CoV-2 in Peru. Case-fatality rate was weakly associated with elevation. Conclusions Our results also strongly suggest that exposure to poor air quality is a significant factor in the mortality of individuals below the age of 30. We conclude that environmental and climatic factors do play a significant role in the transmission and case fatality rates in Peru, however further study is required to see if these relationships are maintained over time.

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Environmental and climatic impact on the infection and mortality of SARS-CoV-2 in Peru

10.1101/2020.09.16.20196170 ◽

2020 ◽

Author(s):

Victor J. Samillan ◽

Diana Flores-Leon ◽

Eduardo Rojas ◽

Brian R. Zutta

Keyword(s):

Air Quality ◽

Population Density ◽

Solar Radiation ◽

Case Fatality Rate ◽

Climatic Factors ◽

Water Vapor Pressure ◽

Case Fatality ◽

Environmental Data ◽

Cumulative Number ◽

Fatality Rate

The role of the environment and climate in the transmission and case-fatality rates of SARS-CoV-2 is still being investigated. Elevation and air quality are believed to be significant factors in the current development of the pandemic, but the influence of additional environmental factors remain unclear. In this study, we explored the relationship between the cumulative number of infections and mortality cases with climate (temperature, precipitation, solar radiation, water vapor pressure, wind), environmental data (elevation, NDVI, PM2.5 and NO2 concentration), and population density in Peru. Using the data from confirmed cases of infection from 1287 districts and confirmed cases of mortality in 479 districts, we used Spearman's correlations to assess the correlation between environmental and climatic factors with cumulative infection cases, cumulative mortality and case-fatality rate. We also explored district cases by the ecozones of coast, sierra, high montane forest and lowland rainforest. Multiple linear regression models indicate elevation, mean solar radiation, air quality, population density and green cover are influential factors in the distribution of infection and mortality of SARS-CoV-2 in Peru. The case-fatality rate was weakly associated with elevation. Our results also strongly suggest that exposure to poor air quality is a significant factor in the mortality of individuals with SARS-CoV-2 below the age of 30. We conclude that environmental and climatic factors do play a significant role in the transmission and case-fatality rates in Peru, however further study is required to see if these relationships are maintained over time.

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Understanding the asymmetric spread and case fatality rate (CFR) for COVID-19 among countries (Preprint)

10.2196/preprints.20013 ◽

2020 ◽

Author(s):

Eldhose Iype ◽

Sadhya Gulati

Keyword(s):

New York ◽

Population Density ◽

Case Fatality Rate ◽

Case Fatality ◽

The United States ◽

Control Measures ◽

High Rate ◽

Cumulative Number ◽

Fatality Rate ◽

Rate Of Increase

UNSTRUCTURED The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are rising rapidly every day in the world, causing the disease COVID-19 with around 2 million people infected and more than 100,000 people died so far, in more than 200 countries. One of the baffling aspects of this pandemic is the asymmetric increase in cases and case fatality rate (CFR) among countries. We analyze the time series of the infection and fatality numbers and found two interesting aspects. Firstly, the rate of spread in a region is directly connected to the population density of the region where the virus is spreading. For example, the high rate of increase in cases in the United States of America (USA) is related to the high population density of New York City. This is shown by scaling the cumulative number of cases with a measure of the population density of the affected region in countries such as Italy, Spain, Germany, and the USA and we see that the curves are coinciding. Secondly, we analyzed the CFR number as a function of the number of days, since the first death, and we found that there are two clear categories among countries: one category with high CFR numbers (around 10%) and the other category with low CFR numbers (2% to 4%). When we analyzed the results, we see that countries with lower CFR numbers more or less tend to have implemented active control measures such as aggressive testing, tracking down possible infections, effective quarantine measures, etc. Moreover, we did not see any convincing correlation between mortality rates and the median age of the population.

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Understanding the asymmetric spread and case fatality rate (CFR) for COVID-19 among countries

10.1101/2020.04.21.20073791 ◽

2020 ◽

Cited By ~ 4

Author(s):

Eldhose Iype ◽

Sadhya Gulati

Keyword(s):

New York ◽

Population Density ◽

Case Fatality Rate ◽

Case Fatality ◽

The United States ◽

Control Measures ◽

High Rate ◽

Cumulative Number ◽

Fatality Rate ◽

Rate Of Increase

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections are rising rapidly every day in the world, causing the disease COVID-19 with around 2 million people infected and more than 100,000 people died so far, in more than 200 countries. One of the baffling aspects of this pandemic is the asymmetric increase in cases and case fatality rate (CFR) among countries. We analyze the time series of the infection and fatality numbers and found two interesting aspects. Firstly, the rate of spread in a region is directly connected to the population density of the region where the virus is spreading. For example, the high rate of increase in cases in the United States of America (USA) is related to the high population density of New York City. This is shown by scaling the cumulative number of cases with a measure of the population density of the affected region in countries such as Italy, Spain, Germany, and the USA and we see that the curves are coinciding. Secondly, we analyzed the CFR number as a function of the number of days, since the first death, and we found that there are two clear categories among countries: one category with high CFR numbers (around 10%) and the other category with low CFR numbers (2% to 4%). When we analyzed the results, we see that countries with lower CFR numbers more or less tend to have implemented active control measures such as aggressive testing, tracking down possible infections, effective quarantine measures, etc. Moreover, we did not see any convincing correlation between mortality rates and the median age of the population.

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Policy Disparities in Fighting COVID-19 Among Japan, Italy, Singapore and China

10.21203/rs.3.rs-77509/v1 ◽

2020 ◽

Author(s):

Xiaohan Wang ◽

Leiyu Shi ◽

Yuyao Zhang ◽

Haiqian Chen ◽

Gang Sun

Keyword(s):

Prevention And Control ◽

Case Fatality Rate ◽

Case Fatality ◽

Mitigation Measures ◽

Cumulative Number ◽

Fatality Rate ◽

Epidemic Control ◽

Growth Trend ◽

Control Policies ◽

And Control

Abstract Objective This study systematically summarizes the COVID-19 prevention and control policies of Japan, Italy, China and Singapore in order to provide policy basis for other countries currently coping with the COVID-19 pandemic.Methods This study summarizes the epidemic prevention and control policies in Japan, Italy, China, and Singapore, and analyzes the effects of policies in the four countries using official statistics.Results As of May 27, 2020, the growth trend of new cases in Japan, Italy, China and Singapore has all stabilized. However, the cumulative number of confirmed cases (231139) and case-fatality rate (14.3%) in Italy far exceeded those in the other three countries, and the effect of epidemic control was inferior. Singapore began to experience a domestic resurgence after April 5, with a cumulative number of confirmed cases reaching 32876, but the case-fatality rate remained extremely low (0.1%). The growth of cumulative confirmed cases in China (84547) is almost stagnant, and the case-fatality rate is low (5.5%). The growth of cumulative confirmed cases in Japan (16661) increased slowly, and the case-fatality rate (4.8%) was slightly lower than that in China.Conclusion This study divides the epidemic prevention and control policies of the four countries into two categories: the blocking measures taken by China and Singapore, and the mitigation measures taken by Japan and Italy. According to the results of epidemic control in the four countries, we can conclude that the blocking measures are more effective. Pay attention to the admission of mild patients and cases tracking as the core strategy of blocking measures, which can be considered in countries all over the world.

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Global Epidemiology of First 90 Days into COVID-19 Pandemic: Disease Incidence, Prevalence, Case Fatality Rate and Their Association with Population Density, Urbanisation and Elderly Population

Journal of Health Management ◽

10.1177/0972063420932762 ◽

2020 ◽

Vol 22 (2) ◽

pp. 117-128 ◽

Cited By ~ 1

Author(s):

Shivam Gupta ◽

Kamalesh Kumar Patel ◽

Shobana Sivaraman ◽

Abha Mangal

Keyword(s):

Population Density ◽

Case Fatality Rate ◽

Elderly Population ◽

Disease Incidence ◽

Case Fatality ◽

Population Level ◽

Epidemiological Data ◽

Fatality Rate ◽

Global Epidemiology ◽

First 90 Days

As the COVID-19 pandemic marches exponentially, epidemiological data is of high importance to analyse the current situation and guide intervention strategies. This study analyses the epidemiological data of COVID-19 from 17 countries, representing 85 per cent of the total cases within first 90 days of lockdown in Wuhan, China. It follows a population-level observational study design and includes countries with 20,000 cases (or higher) as of 21 April 2020. We sourced the data for these 17 countries from worldometers. info, a digital platform being used by several media and reputed academic institutions worldwide. We calculated the prevalence, incidence, case fatality rate and trends in the epidemiology of COVID-19, and its correlation with population density, urbanisation and elderly population. The analysis represents 85 per cent ( N = 2,183,661) of all cases within the first 90 days of the pandemic. Across the analysed period, the burden of the pandemic primarily focused on high- and middle-income countries of Asia, Europe and North America. While the total number of cases and deaths are highest in USA, the prevalence, incidence and case fatality rates are higher in the European countries. The prevalence and incidence vary widely among countries included in the analysis, and the number of cases per million and the case fatality rate are correlated with the proportion of the elderly population and to a lesser extent with the proportion of the urban population.

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Global Pandemic Trends of COVID-19 in 2020

10.21203/rs.3.rs-625008/v1 ◽

2021 ◽

Author(s):

Hai-Zhen Chen ◽

Bo Cai ◽

Jian-Guo Chen

Keyword(s):

Public Health ◽

United States ◽

Mortality Rate ◽

Incidence Rate ◽

Case Fatality Rate ◽

Case Fatality ◽

The United States ◽

Cumulative Number ◽

Fatality Rate ◽

The World

Abstract Background: The novel coronavirus pneumonia (COVID-19) has been global threaten to public health. This paper provides perspective to the decision-making for public health control of the pandemic or the spread of epidemic.Methods: According to the WHO global reported database, we developed and used the number of cumulative cases, and the number of cumulative deaths to calculate and analyze rates of incidence, mortality, and fatality by country, with respect to the 30 highest outbreak (Top 30) countries.Results： As of December 31, 2020, of the global population of 7.585 billion, the cumulative number of reported cases was 81,475,053, and the cumulative number of deaths was 1,798,050. The incidence rate of COVID-19 was 1074.13 per 100,000 population, the mortality rate was 23.70 per 100,000, and the case fatality rate was 2.21%. Among the Top 30 countries, the five countries with the highest number of reported cumulative cases were, in rank, the United States (19,346,790 cases), India (10,266,674), Brazil (7,563,551), Russia (3,159,297) and France (2,556,708), and the five countries with the highest number of cumulative deaths were the United States (335,789 cases), Brazil (192,681), India (148,738), Mexico (123,845) and Italy (73,604). Globally, the countries with the highest incidence rate were, in rank, Andorra, Luxembourg, Montenegro, San Marino, and Czechia; the countries with the highest mortality rate were, in rank, San Marino, Belgium, Slovenia, Italy, and North Macedonia. The highest fatality rate was found in Yemen, Mexico, Montserrat, Isle of Man, and Ecuador, respectively. In China, 96,673 cases of COVID-19 and 4788 deaths were reported in 2020, ranking the 78th and the 43rd, respectively, in the world. The incidence rate and mortality rate were 6.90/105 and 0.34/105, respectively, ranking 207th and 188th in the world. The case fatality rate was 4.95%, ranking 11th in the world.Conclusions： The COVID-19 prevalence is still on the rise, and the turning points of incidence and mortality are not yet forecasted. Personal protection, anti-epidemic measures and efforts from public health personnel, medical professionals, biotechnology R&D personnel, effectiveness of the vaccination programs and the governments, are the important factors to determine the future prevalence of this coronavirus disease.

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Type 1 wild poliovirus and putative enterovirus 109 in an outbreak of acute flaccid paralysis in Congo, October-November 2010

Eurosurveillance ◽

10.2807/ese.15.47.19723-en ◽

2010 ◽

Vol 15 (47) ◽

Cited By ~ 21

Author(s):

G Grard ◽

J F Drexler ◽

S Lekana-Douki ◽

M Caron ◽

A Lukashev ◽

...

Keyword(s):

Case Fatality Rate ◽

Acute Flaccid Paralysis ◽

Case Fatality ◽

Flaccid Paralysis ◽

Cumulative Number ◽

Fatality Rate ◽

Wild Type ◽

High Case Fatality Rate ◽

Wild Poliovirus

An outbreak of flaccid paralysis syndrome in adults is ongoing in Congo. Molecular analysis of faecal, throat and cerebrospinal samples identified wildtype 1 poliovirus and an additional enterovirus C strain related to enterovirus 109 as the cause. As of 22 November, the cumulative number of cases was 409, of which 169 (41.3%) were fatal. This is one of the largest wild type 1 poliovirus outbreaks ever described associated with an unusually high case fatality rate.

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Demystifying the varying case fatality rates (CFR) of COVID-19 in India: Lessons learned and future directions

The Journal of Infection in Developing Countries ◽

10.3855/jidc.13340 ◽

2020 ◽

Vol 14 (10) ◽

pp. 1128-1135

Author(s):

Edwin Sam Asirvatham ◽

Jeyaseelan Lakshmanan ◽

Charishma Jones Sarman ◽

Melvin Joy

Keyword(s):

Urban Population ◽

Case Fatality Rate ◽

Time Lag ◽

Case Fatality ◽

State Level ◽

Lessons Learned ◽

Cumulative Number ◽

Fatality Rate ◽

Targeted Interventions ◽

Socio Demographic Factors

Introduction: At the end of the second week of June 2020, the SARS-CoV-2 responsible for COVID-19 infected above 7.5 million people and killed over 400,000 worldwide. Estimation of case fatality rate (CFR) and determining the associated factors are critical for developing targeted interventions. Methodology: The state-level adjusted case fatality rate (aCFR) was estimated by dividing the cumulative number of deaths on a given day by the cumulative number confirmed cases 8 days before, which is the average time-lag between diagnosis and death. We conducted fractional regression analysis to determine the predictors of aCFR. Results: As of 13 June 2020, India reported 225 COVID-19 cases per million population (95% CI:224-226); 6.48 deaths per million population (95% CI:6.34-6.61) and an aCFR of 3.88% (95% CI:3.81-3.97) with wide variation between states. High proportion of urban population and population above 60 years were significantly associated with increased aCFR (p=0.08, p=0.05), whereas, high literacy rate and high proportion of women were associated with reduced aCFR (p<0.001, p=0.03). The higher number of cases per million population (p=0.001), prevalence of diabetes and hypertension (p=0.012), cardiovascular diseases (p=0.05), and any cancer (p<0.001) were significantly associated with increased aCFR. The performance of state health systems and proportion of public health expenditure were not associated with aCFR. Conclusions: Socio-demographic factors and burden of non-communicable diseases (NCDs) were found to be the predictors of aCFR. Focused strategies that would ensure early identification, testing and effective targeting of non-literate, elderly, urban population and people with comorbidities are critical to control the pandemic and fatalities.

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Assessment of Epidemiological Determinants of COVID-19 Pandemic Related to Social and Economic Factors Globally

Journal of Risk and Financial Management ◽

10.3390/jrfm13090194 ◽

2020 ◽

Vol 13 (9) ◽

pp. 194

Author(s):

Mohammad Mahmudul Hassan ◽

Md. Abul Kalam ◽

Shahanaj Shano ◽

Md. Raihan Khan Nayem ◽

Md. Kaisar Rahman ◽

...

Keyword(s):

Regression Analysis ◽

Population Density ◽

Recovery Rate ◽

Attack Rate ◽

Case Fatality Rate ◽

Case Fatality ◽

Fatality Rate ◽

Middle Income ◽

Middle Income Countries ◽

The Impact

The COVID-19 pandemic has manifested more than a health crisis and has severely impacted on social, economic, and development crises in the world. The relationship of COVID-19 with countries’ economic and other demographic statuses is an important criterion with which to assess the impact of this current outbreak. Based on available data from the online platform, we tested the hypotheses of a country’s economic status, population density, the median age of the population, and urbanization pattern influence on the test, attack, case fatality, and recovery rates of COVID-19. We performed correlation and multivariate multinomial regression analysis with relative risk ratio (RRR) to test the hypotheses. The correlation analysis showed that population density and test rate had a significantly negative association (r = −0.2384, p = 0.00). In contrast, the median age had a significant positive correlation with recovery rate (r = 0.4654, p = 0.00) and case fatality rate (r = 0.2847, p = 0.00). The urban population rate had a positive significant correlation with recovery rate (r = 0.1610, p = 0.04). Lower-middle-income countries had a negative significant correlation with case fatality rate (r= −0.3310, p = 0.04). The multivariate multinomial logistic regression analysis revealed that low-income countries are more likely to have an increased risk of case fatality rate (RRR = 0.986, 95% Confidence Interval; CI = 0.97−1.00, p < 0.05) and recovery rate (RRR = 0.967, 95% CI = 0.95–0.98, p = 0.00). The lower-income countries are more likely to have a higher risk in case of attack rate (RRR = 0.981, 95% CI = 0.97–0.99, p = 0.00) and recovery rate (RRR = 0.971, 95% CI = 0.96–0.98, p = 0.00). Similarly, upper middle-income countries are more likely to have higher risk in case of attack rate (RRR = 0.988, 95% CI = 0.98–1.0, p = 0.01) and recovery rate (RRR = 0.978, 95% CI = 0.97–0.99, p = 0.00). The low- and lower-middle-income countries should invest more in health care services and implement adequate COVID-19 preventive measures to reduce the risk burden. We recommend a participatory, whole-of-government and whole-of-society approach for responding to the socio-economic challenges of COVID-19 and ensuring more resilient and robust health systems to safeguard against preventable deaths and poverty by improving public health outcomes.

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Policy disparities in fighting COVID-19 among Japan, Italy, Singapore and China

International Journal for Equity in Health ◽

10.1186/s12939-020-01374-2 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Xiaohan Wang ◽

Leiyu Shi ◽

Yuyao Zhang ◽

Haiqian Chen ◽

Gang Sun

Keyword(s):

Prevention And Control ◽

Case Fatality Rate ◽

Case Fatality ◽

Health Resources ◽

Mitigation Measures ◽

Cumulative Number ◽

Fatality Rate ◽

Epidemic Control ◽

Control Policies ◽

And Control

Abstract Objective In order to provide experiences for international epidemic control, this study systematically summarized the Coronavirus disease 2019 (COVID-19) prevention and control policies in Japan, Italy, China and Singapore, and also analyzed the possible inequalities that exist in these response approaches to improve global infectious disease control. Methods We summarized the epidemic prevention and control policies in Japan, Italy, China, and Singapore, and analyzed the policy effects of these four countries by using the data published by Johns Hopkins Coronavirus Resource Center. Results As of May 27, 2020, the growing trend of new cases in Japan, Italy, China and Singapore has stabilized. However, the cumulative number of confirmed cases (231139) and case-fatality rate (14.3%) in Italy far exceeded those in the other three countries, and the effect of epidemic control was inferior. Singapore began to experience a domestic resurgence after April 5, with a cumulative number of confirmed cases reaching 32,876, but the case-fatality rate remained extremely low (0.1%). The growth of cumulative confirmed cases in China (84547) was almost stagnant, and the case-fatality rate was low (5.5%). The growth of cumulative confirmed cases in Japan (16661) increased slowly, and the case-fatality rate (4.8%) was slightly lower than that in China. Conclusion This study divided the epidemic prevention and control policies of the four countries into two categories: the blocking measures adopted by China and Singapore, and the mitigation measures adopted by Japan and Italy. According to the Epidemic control results of these four countries, we can conclude that the blocking measures were generally effective. As the core strategy of blocking measures, admitting mild patients into hospital and cases tracing helped curb the spread of the outbreak in Singapore and China. Countries should choose appropriate response strategies on the premise of considering their own situation, increase investment in health resources to ensure global health equity, and eventually control the spread of infectious diseases in the world effectively.

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