Spatial mapping of the incidence, case-fatality rate and recovery rate of COVID-19 in West Bengal: An epidemiology study

Information on the spatial and temporal distributions of COVID-19 cases is important for improved control, social distancing strategies and developing targeted prevention strategies. Towards this objective, we analyzed the spatial and temporal growth pattern of COVID-19 incidence and death counts in districts of West Bengal. This paper also analyzes the current trend or pattern of COVID-19 transmission in West Bengal. For this approach, COVID-19 data have been compiled from several sources, including the WHO, Ministry of Health and Family Welfare (MoHFW), and demographic data from Census of India (2011). This analytical study was conducted based on detailed data from 23 districts of West Bengal from May 31, 2020, till December 31, 2021. We used ArcGIS Software for map-making and different formulas to measure Incidence, CFR, and CRR, considering all possible scenarios. Up to December 31, 2021, Kolkata, the origin of the COVID-19 epidemic, had reported 337767 COVID-19 cases, while the confirmed cases in the surrounding districts North 24 Parganas, South 24 Parganas, and Howrah were 337091, 104268, and 102048, respectively. The top five districts with the highest incidence were Kolkata (7.51%), Darjeeling (3.66%), North 24 Parganas (3.36%), Kalimpong (2.85%), and Jalpaiguri (1.79%), had high risks of COVID-19. Therefore, identification of the case fatality, recovery rates, and spatiotemporal trends should be the first step to evaluate disease severity and develop effective policies to manage and control any new epidemic. These results are informative locally and useful for the rest of the world. © 2020 The author (s). Published by Zagazig University. This is an open access article under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/.

Epidemiological waves - Types, drivers and modulators in the COVID-19 pandemic.

Introduction: A discussion of 'waves' of the COVID-19 epidemic in different countries is a part of the national conversation for many, but there is no hard and fast means of delineating these waves in the available data and their connection to waves in the sense of mathematical epidemiology is only tenuous. Methods: We present an algorithm which processes a general time series to identify substantial, significant and sustained periods of increase in the value of the time series, which could reasonably be described as 'observed waves'. This provides an objective means of describing observed waves in time series. We use this method to synthesize evidence across different countries to study types, drivers and modulators of waves. Results: The output of the algorithm as applied to epidemiological time series related to COVID-19 corresponds to visual intuition and expert opinion. Inspecting the results of individual countries shows how consecutive observed waves can differ greatly with respect to the case fatality ratio. Furthermore, in large countries, a more detailed analysis shows that consecutive observed waves have different geographical ranges. We also show how waves can be modulated by government interventions and find that early implementation of NPIs correlates with a reduced number of observed waves and reduced mortality burden in those waves. Conclusion: It is possible to identify observed waves of disease by algorithmic methods and the results can be fruitfully used to analyse the progression of the epidemic.

Studying the Spread of COVID-19 and Its Impact on E-learning: From a Deep Learning Perspective

COVID-19 is a respiratory infectious disease caused by a recently discovered Coronavirus. Since December 2019 and as of October 8, 2020, about 36.6 million (36,625,199) confirmed cases of COVID-19 have been registered globally by the WHO, with more than 1 million (1,063,780) deaths. This paper investigates statistically the spread of COVID-19 disease, which became a killer pandemic in Saudi Arabia. We demonstrate that the low apparent Case Fatality Ratio (CFR) (i.e., mortality rate) observed in Saudi Arabia, as compared with other countries, is strongly proportional to the number of infection cases. To present an effective statistical analysis of the end of COVID-19 pandemic, the researchers used the present evaluation of the Infection Fatality Ratio (IFR) of the COVID-19 reported until September 2020, depending on the reported CFR obtained from the Ministry of Health. The proposed analysis shows more realistic evaluations of the actual range of the deceased as well as more precise factors of how rapidly the infection spreads. The study demonstrates the more powerful elements causing the seriousness of the COVID-19 in Saudi Arabia. Finally, the researchers use the mortality number collected through the last seven months to predict both the overall number of infections and the period in which the infection will end in the Kingdom of Saudi Arabia. The researchers presented the effect of the spread of the COVID-19 pandemic in the E-learning sector in the KKU and BUE universities and the period in which the infection will end. Deep learning (DL) is a potentially powerful artificial intelligence (AI) tool in the fight against the COVID-19 pandemic. This paper also addressed this issue and answered the question: can deep learning technology be used to early screen COVID-19 patients from their computed tomography (CT) images and what is the accuracy of this diagnostic tool. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A Note on Estimating the Case Fatality Ratio for Novel Corona Virus Disease (COVID-19)

Background: In this paper, an attempt has been made to estimate the Case Fatality Ratio (CFR) for coronavirus disease of India and a few selected countries. And also, it highlighted the pros and cons of obtaining crude and adjusted CFR of COVID-19 pandemic. Material(s) and Method(s): Data extracted from the WHO situation report and the University of Oxford website have been used for this analysis. The CFR and its 95% confidence interval were computed, trend and bar plot was used for graphical representation. Result(s): The worldwide crude CFR stands 6.73% (95% CI 6.69 to 6.76) based on 21, 83, 877 confirmed and 1,46,872 death cases(as on 17th April, 2020). Belgium was the highest CFR at 13.95% as compared to others. However, India's CFR was found to be around 3.26% (as on 17th April, 2020). Conclusion(s): In conclusion, the estimation and interpretation of CFR are critical in response to ongoing COVID-19. The initial CFR estimates are subject to change, still, it is useful for healthcare planning over the coming months. Moreover, the precise and robust estimates of CFR will be available only at the end of the epidemic.Copyright © 2021 Bentham Science Publishers.

The disproportionate case-fatality ratio of COVID-19 between countries with the highest vaccination rates and the rest of the world.

Objectives: The global reported cumulative case-fatality ratios (rCFRs) and excess mortality rates of the 20 countries with the highest coronavirus disease 2019 (COVID-19) vaccination rates, the rest of the world and Sub-Saharan Africa (SSA) were compared before and after the commencement of vaccination programmes. Methods: A time series model was used to understand the trend of rCFR over time, and a generalized linear mixed model was used to understand the effect of vaccination on rCFR. Results: By 31 December 2022, an average of 260.3 doses of COVID-19 vaccine per 100 population had been administered in the top 20 vaccinated countries, compared with 152.1 doses in the rest of the world and 51.2 doses in SSA. The mean rCFR of COVID-19 had decreased by 69.0% in the top 20 vaccinated countries, 26.5% in the rest of the world and 7.6% in SSA. Excess mortality had decreased by 48.7% in the top 20 vaccinated countries, compared with 62.5% in the rest of the world and 60.7% in SSA. In a generalized linear mixed model, the reported number of vaccine doses administered (/100 population) (odds ratio 0.64) was associated with a steeper reduction in COVID-19 rCFR. Conclusions: Vaccine equity and faster roll-out across the world is critically important in reducing COVID-19 transmission and CFR.

Daily changes in spatial accessibility to ICU beds and their relationship with the case-fatality ratio of COVID-19 in the state of Texas, USA.

During the COVID-19 pandemic, many patients could not receive timely healthcare services due to limited availability and access to healthcare resources and services. Previous studies found that access to intensive care unit (ICU) beds saves lives, but they overlooked the temporal dynamics in the availability of healthcare resources and COVID-19 cases. To fill this gap, our study investigated daily changes in ICU bed accessibility with an enhanced two-step floating catchment area (E2SFCA) method in the state of Texas. Along with the increased temporal granularity of measurements, we uncovered two phenomena: 1) aggravated spatial inequality of access during the pandemic, and 2) the retrospective relationship between insufficient ICU bed accessibility and the high case-fatality ratio of COVID-19 in rural areas. Our findings suggest that those locations should be supplemented with additional healthcare resources to save lives in future pandemic scenarios.

Evaluation of Characteristics and Outcomes of COVID-19 Between First Wave and Second Wave: Study From a Tertiary Cancer Care Centre, Delhi, India.

Background and objectives The second wave of coronavirus disease-19 (COVID-19) had several severe consequences in the form of rising cases, deaths, and overwhelming health infrastructure in India. However, the similarities and differences between the characteristics of the first and second waves have yet to be explained. The objectives of the study were to compare the incidence, clinical management, and mortality rates between two waves. Methods The COVID-19 data collated from Rajiv Gandhi Cancer Institute and Research Centre, Delhi between the first wave (1 April 2020 to 27 February 2021) and second wave (1 March 2021 to 30 June 2021) were evaluated in terms of incidence, the clinical course of the disease, and mortality rates. Results The number of subjects hospitalized in the first and second waves was 289 and 564, respectively. Compared to the first wave, the proportion of patients with severe disease was higher (9.7% vs. 37.8%). Several parameters such as age group, grade of disease, the reason for hospitalization, values of peripheral oxygen saturation, type of respiratory support, response to therapy, vital status, and others show statistically significant differences between the two waves (P<0.001). The mortality rate in the second wave was significantly higher (20.2% vs. 2.4%, P<0.001) than in the first wave. Interpretation and conclusions The clinical course and outcomes of COVID-19 significantly differ between the first and second waves. There is a higher incidence of hospitalized patients (66.1% vs. 33.9%) with drastically increased case fatality rate in the second wave. Disease severity in the first wave is four times lower than in the second wave. The second wave was quite devastating, which led to the shortage of critical care facilities and the loss of a significant number of lives.

COVID-19-related deaths: a 2-year inter-wave comparison of mortality data from Germany.

PURPOSE: The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) has caused substantial mortality worldwide. We investigated clinical and demographic features of COVID-19-related deaths that occurred between March 2020 and January 2022 in Regensburg, Germany. METHODS: We compared data across four consecutive time periods: March 2020 to September 2020 (period 1), October 2020 to February 2021 (period 2), March 2021 to August 2021 (period 3), and September 2021 to January 2022 (period 4). RESULTS: Overall, 405 deaths in relation to COVID-19 were reported. The raw case fatality ratio (CFR) was 0.92. In periods 1 to 4, the CFRs were 1.70%, 2.67%, 1.06%, and 0.36%. The age-specific CFR and mortality were highest in persons aged ≥ 80 years in period 2 while mortality in younger cases increased with time. The median age at death was 84 years and it varied slightly across periods. Around 50% of cases of death were previously hospitalized. In all time periods, the cause of death was mostly attributed to COVID-19. Over the four periods, we did not find significant changes in the distribution of sex and risk factors for severe disease. The most frequent risk factor was cardio-circulatory disease. CONCLUSION: In conclusion, the CFR decreased over time, most prominently for period 4. Mortality was considerable and younger cases were increasingly at risk.

Trends in managing COVID-19 from an emerging infectious disease to a common respiratory infectious disease: What are the subsequent impacts on and new challenges for healthcare systems?

Targeting the 9 countries with the highest cumulative number of newly confirmed cases in the past year, we analyzed the case fatality ratio (CFR) among newly confirmed cases and the vaccination rate (two or more doses of vaccine per 100 people) in the United States of America (USA), India, France, Germany, Brazil, the Republic of Korea, Japan, Italy, and the United Kingdom (UK) for the period of 2020-2022. Data reveal a decrease in the CFR among newly confirmed cases since the beginning of 2022, when transmission of the Omicron variant predominates, and an increase in vaccination rates. The Republic of Korea had the lowest CFR among newly confirmed cases (0.093%) in 2022 and the highest vaccination rate (86.27%). Japan had the second highest vaccination rate (83.12%) and a decrease in the CFR among newly confirmed cases of 1.478% in 2020, 1.000% in 2021, and 0.148% in 2022; while the average estimated fatality ratio for seasonal influenza from 2015-2020 was 0.020%. Currently, most countries are now easing COVID-19-related restrictions and are exploring a shift in management of COVID-19 from an emerging infectious disease to a common respiratory infectious disease that can be treated as the equivalent of seasonal or regional influenza. However, compared to influenza, infection with the Omicron variant still has a higher fatality ratio, is more transmissible, and the size of future outbreaks cannot be accurately predicted due to the uncertainty of viral mutation. More importantly, as countries shift their response strategies to COVID-19, there is an urgent need at this time to clarify what the subsequent impacts on healthcare systems and new challenges will be, including the clinical response, the dissemination of scientific information, vaccination campaigns, the creation of future surveillance and response systems, the cost of treatments and vaccinations, and the flexible use of big data in healthcare systems.

Demographic and Mortality Trends across three COVID-19 waves in Kashmir Division, India

Background: The demographic profile and fatality varied across different waves with the appearance of new variants. Further the trends varied from country to country and within the country. We have analyzed the trends from a selected geographic area. Aims and Objectives: To assess the trends in COVID-19 cases and deaths and to compare the characteristics of three different COVID-19 waves in Kashmir Division. Materials and Methods: It is a cross sectional study based on secondary data. Results: The mean age of deaths increased from 64.53 years in first wave to 65.02 years in the second wave and further to 69.23 years in the third wave (p-value = 0.006). An overall CFR of 0.85% was observed in our population with a maximum case fatality of 1.67% in first wave followed by 0.79% in second wave and only 0.13% in third wave (p < 0.0001). Lowest CFR of 0.04% was observed in the age group of <20 years and maximum CFR of 6.12% was observed in the subjects with age more than 60 years. It was also observed that the CFR increased with the increase of age across all the three waves (p < 0.0001). Conclusion: Our study suggests that the Case Fatality Ratio declined with time despite new variants appearing which spread at very fast rate. The mortality remained higher in elderly and in males in all the three waves. [ FROM AUTHOR] Copyright of Asian Journal of Medical Sciences is the property of Manipal Colleges of Medical Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Assessing government policies' impact on the COVID-19 pandemic and elderly deaths in East Asia.

This study assesses governments' long-term non-pharmaceutical interventions upon the coronavirus disease 2019 (COVID-19) pandemic in East Asia. It advances the literature towards a better understanding of when and which control measures are effective. We (1) provide time-varying case fatality ratios and focus on the elderly's mortality and case fatality ratios, (2) measure the correlations between daily new cases (daily new deaths) and each index based on multiple domestic pandemic waves and (3) examine the lead-lag relationship between daily new cases (daily new deaths) and each index via the cross-correlation functions on the pre-whitened series. Our results show that the interventions reduce COVID-19 infections for some periods before the period of the Omicron variant. Moreover, there is no COVID-19 policy lag in Taiwan between daily new confirmed cases and each index. As of March 2022, the case fatality ratios of the elderly group in Japan, Hong Kong and South Korea are 4.69%, 4.72% and 1.48%, respectively, while the case fatality ratio of the elderly group in Taiwan is 25.01%. A government's COVID-19 vaccination distribution and prioritisation policies are pivotal for the elderly group to reduce the number of deaths. Immunising this specific group as best as possible should undoubtedly be a top priority.

Phase Dynamics of the Covid-19 Pandemic. A Systematic Analysis of 213 Countries and Territories. Report 2.

The study was carried out to evaluate the dynamics of monthly numbers of cases, deaths, tests and case fatality ratio worldwide during three phases of the COVID-19 pandemic. Material and methods: Twenty-three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, case fatality ratio, infection fatality ratio, etc. were counted for various periods of time for each of the 213 countries, then the results related to different periods of time were compared. The analysis of main epidemiological parameters resulted in division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases and mortality due to COVID-19. By the end of second phase, the worldwide statistics indicated end of the pandemic, but the third phase was characterized by sudden rise in number of new cases and deaths. The most dramatic evolution of epidemic curve occurred in the countries where physicians had successfully confronted COVID-19 during the first two phases of the pandemic. Despite the decrease in the overall numbers deaths during the latest months analyzed, additional study is necessary to identify causes of new cases and deaths during the third phase of the pandemic. It can be suggested that preventive and therapeutic protocols should be changed from the 'standard' to 'personalized' types.

Neighborhood disparities in COVID-19 outcomes in New York city over the first two waves of the outbreak.

PURPOSE: To assess the association of neighborhood demographic and socioeconomic characteristics with COVID-19 incidence and mortality in New York City (NYC) over the first two waves of outbreak. METHODS: This retrospective study used neighborhood-level data from 177 modified ZIP code tabulation areas in NYC between March 01, 2020 and April 30, 2021. RESULTS: Neighborhoods that were most severely impacted in wave 1 were also more affected in wave 2. Neighborhoods with a higher percentage of seniors (≥75 years), males, Black and Hispanic population, and large-size households had higher incidence rates of COVID-19 in wave 1 but not in wave 2. Neighborhoods with higher percentage of Black and Hispanic population and lower insurance coverage had higher death rate per capita and case fatality ratio in wave 1, and neighborhoods with higher percentage of Black and Asian population had elevated case fatality ratio in wave 2. Median household income was negatively associated with incidence rate and death rate per capita but not associated with case fatality ratio in both waves. Neighborhoods with more seniors had higher death rate and case fatality ratio in both waves. CONCLUSIONS: Neighborhood disparities in COVID-19 incidence and mortality across NYC neighborhoods were dynamic during the first two waves of outbreak.

Bird's Eye View of COVID-19, Mobility, and Labor Market Outcomes Across the US.

COVID-19 dealt a formidable blow to the US economy. We present a joint analysis of the epidemiological and labor market outcomes across US states. We focus on the relationship across relevant indicators in the pre-vaccination era. As expected, we find strong correlation between changes in economic conditions and mobility. However, mobility fluctuations tend to be uncorrelated with local epidemics and occur simultaneously across most states. The magnitude of the mobility response is highly correlated with the rural vs. urban character of the area. Employment losses are most strongly associated with high population density and concentration of the leisure and hospitality industry. The relationship between job losses and the case fatality ratio is affected by the timing of the most severe COVID-19 waves.

The phase dynamics of the COVID-19 pandemic: a systematic analysis of 213 Countries and Territories.

The study was carried out to evaluate the dynamics of monthly numbers of cases, deaths, tests and case fatality ratio worldwide during three phases of the COVID-19 pandemic. Material and methods: Twenty-three sets of databases, dated the 22nd of each month from January 2020 to November 2021, for 213 countries were collected from the Worldometer website. The number of cases, deaths, tests, case fatality ratio, infection fatality ratio, etc. were counted for various periods of time for each of the 213 countries, then the results related to different periods of time were compared. The analysis of main epidemiological parameters resulted in division of three phases of the global pandemic evolution. The first phase (23.01.20-22.07.20), the second phase (23.07.20-22.01.21) and the third phase (23.01.21-22.07.21) were different in terms of the number of tests performed, new cases and mortality due to COVID-19. By the end of second phase, the worldwide statistics indicated imminent end of the pandemic, but the third phase was characterized by sudden rise in the number of new cases and deaths that could not be explained rationally. The most dramatic evolution of epidemic curve occurred in the countries where physicians had successfully confronted COVID-19 during the first two phases of the pandemic. Despite the decrease in the overall numbers deaths during the latest months analyzed, additional study is necessary to identify the cause of increasing in the number of new cases and deaths during the third phase of the pandemic. Presumably, there are several causes of negative evolution of the current pandemic, including over-reliance on polymerase chain reaction tests, application of non-specialized premises for quarantine and treatment, non-professional management, following therapeutic protocols applied in countries with high number of deaths, ignoring preventive treatment, and decreasing in mass and individual immunity. It can be suggested that the use of drugs modulating T-cell immunity is necessary, and preventive and therapeutic protocols should be changed from the 'standard' to 'personalized' types.

The Contributions of Pandemic Severity, Government Stringency, Cultural Values and Internet Usage to Post-traumatic Stress Disorder During the COVID-19 Pandemic: An Analysis of Data From 35 Countries.

Emergent symptoms of post-traumatic stress disorder (PTSD) have been frequently reported in the context of the COVID-19 pandemic, and may affect up to 17-18% of individuals. There is preliminary evidence that pandemic severity, cultural values, restrictions imposed by governments, and Internet usage may all influence the emergence of PTSD symptomatology. In this study, possible linear- and non-linear associations between these factors and the prevalence of PTSD symptoms across 35 countries were examined based on data from existing research. Evidence was found for a positive logarithmic relationship between the COVID-19 case-fatality ratio and PTSD (p = 0.046), a positive logarithmic relationship between power distance and PTSD (p = 0.047), and a trend toward a negative quadratic association with Internet usage (p = 0.051). No significant cross-national effect was observed for government restrictiveness. These findings suggest that strategies aimed at minimizing COVID-19 deaths, and at ensuring equitable access to essential resources, may be of use in reducing the emergence of PTSD symptoms at a population level during this pandemic.

Time Trend in SARS-CoV-2 Seropositivity, Surveillance Detection- and Infection Fatality Ratio until Spring 2021 in the Tirschenreuth County-Results from a Population-Based Longitudinal Study in Germany.

Herein, we provide results from a prospective population-based longitudinal follow-up (FU) SARS-CoV-2 serosurveillance study in Tirschenreuth, the county which was hit hardest in Germany in spring 2020 and early 2021. Of 4203 individuals aged 14 years or older enrolled at baseline (BL, June 2020), 3546 participated at FU1 (November 2020) and 3391 at FU2 (April 2021). Key metrics comprising standardized seroprevalence, surveillance detection ratio (SDR), infection fatality ratio (IFR) and success of the vaccination campaign were derived using the Roche N- and S-Elecsys anti-SARS-CoV-2 test together with a self-administered questionnaire. N-seropositivity at BL was 9.2% (1st wave). While we observed a low new seropositivity between BL and FU1 (0.9%), the combined 2nd and 3rd wave accounted for 6.1% new N-seropositives between FU1 and FU2 (ever seropositives at FU2: 15.4%). The SDR decreased from 5.4 (BL) to 1.1 (FU2) highlighting the success of massively increased testing in the population. The IFR based on a combination of serology and registration data resulted in 3.3% between November 2020 and April 2021 compared to 2.3% until June 2020. Although IFRs were consistently higher at FU2 compared to BL across age-groups, highest among individuals aged 70+ (18.3% versus 10.7%, respectively), observed differences were within statistical uncertainty bounds. While municipalities with senior care homes showed a higher IFR at BL (3.0% with senior care home vs. 0.7% w/o), this effect diminished at FU2 (3.4% vs. 2.9%). In April 2021 (FU2), vaccination rate in the elderly was high (>77.4%, age-group 80+).

SARS-CoV-2 cases reported from long-term residential facilities (care homes) in South Africa: a retrospective cohort study.

BACKGROUND: Globally, long-term care facilities (LTCFs) experienced a large burden of deaths during the COVID-19 pandemic. The study aimed to describe the temporal trends as well as the characteristics and risk factors for mortality among residents and staff who tested positive for SARS-CoV-2 in selected LTCFs across South Africa. METHOD: We analysed data reported to the DATCOV sentinel surveillance system by 45 LTCFs. Outbreaks in LTCFs were defined as large if more than one-third of residents and staff had been infected or there were more than 20 epidemiologically linked cases. Multivariable logistic regression was used to assess risk factors for mortality amongst LTCF residents. RESULTS: A total of 2324 SARS-CoV-2 cases were reported from 5 March 2020 through 31 July 2021; 1504 (65%) were residents and 820 (35%) staff. Among LTCFs, 6 reported sporadic cases and 39 experienced outbreaks. Of those reporting outbreaks, 10 (26%) reported one and 29 (74%) reported more than one outbreak. There were 48 (66.7%) small outbreaks and 24 (33.3%) large outbreaks reported. There were 30 outbreaks reported in the first wave, 21 in the second wave and 15 in the third wave, with 6 outbreaks reporting between waves. There were 1259 cases during the first COVID-19 wave, 362 during the second wave, and 299 during the current third wave. The case fatality ratio was 9% (138/1504) among residents and 0.5% (4/820) among staff. On multivariable analysis, factors associated with SARS-CoV-2 mortality among LTCF residents were age 40-59 years, 60-79 years and ≥ 80 years compared to < 40 years and being a resident in a LTCF in Free State or Northern Cape compared to Western Cape. Compared to pre-wave 1, there was a decreased risk of mortality in wave 1, post-wave 1, wave 2, post-wave 2 and wave 3. CONCLUSION: The analysis of SARS-CoV-2 cases in sentinel LTCFs in South Africa points to an encouraging trend of decreasing numbers of outbreaks, cases and risk for mortality since the first wave. LTCFs are likely to have learnt from international experience and adopted national protocols, which include improved measures to limit transmission and administer early and appropriate clinical care.

COVID or not COVID: attributing and reporting cause of death in a community cohort.

OBJECTIVES: In Germany, deaths of SARS-CoV-2-positive persons are reported as 'death related to SARS-CoV-2/COVID-19' to the Robert Koch Institute, Germany's main infectious disease institution. In 177 COVID-19-associated deaths reported in Regensburg, Germany, from October 2020 to January 2021, we investigated how deaths following SARS-CoV-2 infection were reported and whether cases with a death attributed to SARS-CoV-2 (COVID-19 death [CD]) differed from cases with a reported death from other causes (non-COVID-19 death [NCD]). STUDY DESIGN: This was an observational retrospective cohort study. METHODS: We analysed descriptive data on the numbers of cases, deaths, age, sex, symptoms and hospitalizations. We calculated odds ratios (ORs) with 95% confidence intervals (95% CIs) and performed Chi-squared/Fisher's exact test for categorical variables and the Wilcoxon rank-sum test for comparison of medians. RESULTS: Deaths attributed to COVID-19 occurred primarily in elderly patients. The mortality rate and the case fatality ratio (CFR) increased with age. The median age and the prevalence of risk factors were similar between CD and NCD. Respiratory symptoms and pneumonia at the time of diagnosis were associated with death reported as CD. The odds of CD attribution in cases hospitalized because of COVID-19 were 6-fold higher than the odds of NCD (OR: 6.00; 95% CI: 1.32 to 27.22). CONCLUSIONS: Respiratory symptoms/pneumonia at the time of diagnosis and hospitalization due to COVID-19 were associated with attributing a death to COVID-19. Numbers of COVID deaths need to be interpreted with caution. Criteria that facilitate attributing the cause of death among SARS-CoV-2 cases more uniformly could make these figures more comparable.

An epidemiological, strategic and response analysis of the COVID-19 pandemic in South Asia: a population-based observational study.

INTRODUCTION: South Asia has had a dynamic response to the ongoing COVID-19 pandemic. The overall burden and response have remained comparable across highly-burdened countries within the South Asian Region. METHODOLOGY: Using a population-based observational design, all eight South Asian countries were analyzed using a step-wise approach. Data were obtained from government websites and publicly-available repositories for population dynamics and key variables. RESULTS: South Asian countries have a younger average age of their population. Inequitable distribution of resources centered in urban metropolitan cities within South Asia is present. Certain densely populated regions in these countries have better testing and healthcare facilities that correlate with lower COVID-19 incidence per million populations. Trends of urban-rural disparities are unclear given the lack of clear reporting of the gaps within these regions. COVID-19 vaccination lag has become apparent in South Asian countries, with the expected time to complete the campaign being unfeasible as the COVID-19 pandemic progresses. CONCLUSION: With a redesigning of governance policies on preventing the rise of COVID-19 promptly, the relief on the healthcare system and healthcare workers will allow for adequate time to roll out vaccination campaigns with equitable distribution. Capacity expansion of public health within the Region is required to ensure a robust healthcare response to the ongoing pandemic and future infectious disease outbreaks.