Methods for estimating the case fatality ratio for a novel, emerging infectious disease

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis

BACKGROUND

The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information.

AIM

To identify key factors that may explain the variability in case lethality across countries.

METHODS

We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship.

RESULTS

The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR.

CONCLUSION

The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

Comparison of COVID-19 with influenza A in the ICU: a territory-wide, retrospective, propensity matched cohort on mortality and length of stay

OBJECTIVES

Direct comparisons between COVID-19 and influenza A in the critical care setting are limited. The objective of this study was to compare their outcomes and identify risk factors for hospital mortality.

DESIGN AND SETTING

This was a territory-wide, retrospective study on all adult (≥18 years old) patients admitted to public hospital intensive care units in Hong Kong. We compared COVID-19 patients admitted between 27 January 2020 and 26 January 2021 with a propensity-matched historical cohort of influenza A patients admitted between 27 January 2015 and 26 January 2020. We reported outcomes of hospital mortality and time to death or discharge. Multivariate analysis using Poisson regression and relative risk (RR) was used to identify risk factors for hospital mortality.

RESULTS

After propensity matching, 373 COVID-19 and 373 influenza A patients were evenly matched for baseline characteristics. COVID-19 patients had higher unadjusted hospital mortality than influenza A patients (17.5% vs 7.5%, p<0.001). The Acute Physiology and Chronic Health Evaluation IV (APACHE IV) adjusted standardised mortality ratio was also higher for COVID-19 than influenza A patients ((0.79 (95% CI 0.61 to 1.00) vs 0.42 (95% CI 0.28 to 0.60)), p<0.001). Adjusting for age, P_aO₂/F_iO₂, Charlson Comorbidity Index and APACHE IV, COVID-19 (adjusted RR 2.26 (95% CI 1.52 to 3.36)) and early bacterial-viral coinfection (adjusted RR 1.66 (95% CI 1.17 to 2.37)) were directly associated with hospital mortality.

CONCLUSIONS

Critically ill patients with COVID-19 had substantially higher hospital mortality when compared with propensity-matched patients with influenza A.

The Impact of the First and Second Waves of COVID-19 Pandemic in Nigeria

In recent times, the COVID-19 pandemic has been the subject of global concern. It has so far claimed over 5.4 million lives globally, with over 291 million cases recorded worldwide as of 5 January 2022. It is known to have different waves and variants, thus making it difficult to handle/manage. This study investigates the impact of the first and second waves of COVID-19 in Nigeria, West Africa. The data used is for the 36 states of Nigeria archived at the National Centre for Disease Control from February 2020 to April 2021. Results from the study reveal that the highest number of COVID-19 cases during the first/second wave was recorded at Lagos (23,238/34,616), followed by the Federal Capital Territory (FCT) (6,770/12,911) and alternates between Plateau (3,858/5,170) and Kaduna (3,064/5,908). Similarly, the highest number of deaths (during the first/second wave) was also recorded in Lagos (220/219), followed by Edo (112/73), and then FCT (83/81). The Case Fatality Ratio (CFR) was observed to be higher mostly in northern Nigeria during the first wave and the southeast during the second wave of the pandemic. On the average, the number of cases/deaths recorded during the second wave was higher than those of the first wave, but a decrease in the CFR values was observed during the second wave. Higher values of COVID-19 cases/death were mostly recorded in Nigeria during; maximum relative humidity (RH) (>70%) with minimum Temperatures (<25°C), Low temperatures, and low RH which is mostly observed during the cold/dusty periods.

An online observational study assessing clinical characteristics and impacts of the COVID-19 pandemic on mental health: a perspective study from Bangladesh

Purpose

The pandemic of coronavirus disease 2019 (COVID-19) has cost numerous lives and induced tremendous mental stress among people. The purpose of this research was to determine anxiety and depression levels, clinical features, and the connections between demographic variables and depression prevalence as well as anxiety prevalence among reported COVID-19 cases in Bangladesh.

Methods

For the purpose of data collection, an online cross-sectional survey was carried out from May 26 to June 27, 2020, utilizing a Google adapted preformed questionnaire. The form was shared with a short overview and justification through Facebook, Twitter, Facebook messenger, Viber, and What's App. The Google form contains five parts: a brief introduction, an approval statement, demographics, clinical and radiological data, and mental health assessment by the Generalized Anxiety Disorder 7-item (GAD-7) scale and Patient Health Questionnaire (PHQ-9). Formal ethical clearance was taken from the Institute of Biological Science (IBSc), Bangladesh. Informed consent was ensured before participation.

Results

One hundred and fifty-three (153) patients with COVID-19 who had an average age of 39.43 ± 17.59 years with male predominance (72%) were included. A total of 32.7% were doing health-care related jobs, and 17.7% lost their jobs due to COVID-19. Patients had a median income of 30,000 Bangladesh taka (BDT). Of all, 12.4% of the participants showed asymptomatic features, whereas 87.6% of patients were symptomatic and presented with fever (79%), cough (58.8%), myalgia (24.2%), breathlessness (23.5%), sore throat (21.6%), fatigue (19.6%), headache (13.7%), nausea and/or vomiting (11.8%), runny nose (9.8%), chest pain (9.2%), diarrhea (8.5%), stuffy nose (3.2%), ARDS (2.6%), oral ulcer (2.6%), and conjunctivitis (1.9%). Overall, the prevalence of anxiety and depression was 63.5% and 56.6%, respectively. Among the participants, 13.2% had only anxiety, 6.3% had only depression, and 50.3% had both.

Conclusion

In most cases, middle age, male, and healthy workers were patients. Fever and cough were the standard presentations. Approximately two-thirds or 66.67% of patients had anxiety and depression, one or both.

A sequential test for assessing the effectiveness of response strategies during an emerging epidemic

In epidemiology, the fatality rate is an important indicator of disease severity and has been used to evaluate the effects of new treatments. During an emerging epidemic with limited resources, monitoring the changes in fatality rate can also provide signals on the evaluation of government policies and healthcare quality, which helps to guide public health decision. A statistical test is developed in this paper to detect changes in fatality rate over time during the course of an emerging infectious disease. A major advantage of the proposed test is that it only requires the regularly reported numbers of deaths and recoveries, which meets the actual need as detailed surveillance data are hard to collect during the course of an emerging epidemic especially the deadly infectious diseases with large magnitude. In addition, with the sequential testing procedure, the effective measures can be detected at the earliest possible time to provide guidance to policymakers for swift action. Simulation studies showed that the proposed test performs well and is sensitive in picking up changes in the fatality rate. The test is applied to the 2014-2016 Ebola outbreak in Sierra Leone for illustration.

Evolution of the lethality due to SARS-CoV-2 in Spain according to age group and sex

The emergence of SARS-CoV-2 in China in December 2019 has posed a major challenge to health systems in all countries around the world. One of the most relevant epidemiological measures to consider during the course of a pandemic is the proportion of cases that eventually die from the disease (case fatality ratio, CFR). Monitoring the evolution of this indicator is of paramount importance because it allows for the assessment of both variations in the lethality of the virus and the effectiveness of the control measures implemented by health authorities. One of the problems with estimating the CFR in practice is that the available data only show daily or weekly counts of new cases and deaths; there is no information on when each deceased patient was infected and therefore it is not possible to know exactly how many cases there were at the time the patient became infected. Various approaches have been proposed for calculating the CFR by correcting for the time lag between infection and death. In this paper, we present a novel methodology to perform a non-parametric estimation of the evolution of the CFR by initially identifying an optimal time lag between infections and deaths. The goodness of this procedure is assessed by means of a simulation study and the method is applied to the estimation of the CFR in Spain in the period from July 2020 to March 2022.

Epidemiologic Parameters for COVID-19: A Systematic Review and Meta-Analysis

Background: The World Health Organization (WHO) declared the coronavirus disease 2019 (COVID-19) outbreak to be a public health emergency and international concern and recognized it as a pandemic. This study aimed to estimate the epidemiologic parameters of the COVID-19 pandemic for clinical and epidemiological help. Methods: In this systematic review and meta-analysis study, 4 electronic databases, including Web of Science, PubMed, Scopus, and Google Scholar were searched for the literature published from early December 2019 up to 23 March 2020. After screening, we selected 76 articles based on epidemiological parameters, including basic reproduction number, serial interval, incubation period, doubling time, growth rate, case-fatality rate, and the onset of symptom to hospitalization as eligibility criteria. For the estimation of overall pooled epidemiologic parameters, fixed and random effect models with 95% CI were used based on the value of between-study heterogeneity (I2). Results: A total of 76 observational studies were included in the analysis. The pooled estimate for R₀ was 2.99 (95% CI, 2.71-3.27) for COVID-19. The overall R₀ was 3.23, 1.19, 3.6, and 2.35 for China, Singapore, Iran, and Japan, respectively. The overall serial interval, doubling time, and incubation period were 4.45 (95% CI, 4.03-4.87), 4.14 (95% CI, 2.67-5.62), and 4.24 (95% CI, 3.03-5.44) days for COVID-19. In addition, the overall estimation for the growth rate and the case fatality rate for COVID-19 was 0.38% and 3.29%, respectively. Conclusion: The epidemiological characteristics of COVID-19 as an emerging disease may be revealed by computing the pooled estimate of the epidemiological parameters, opening the door for health policymakers to consider additional control measures.

Time-dependent risk of COVID-19 death with overwhelmed health-care capacity in Japan, 2020-2022

BACKGROUND

It has been descriptively argued that the case fatality risk (CFR) of coronavirus disease (COVID-19) is elevated when medical services are overwhelmed. The relationship between CFR and pressure on health-care services should thus be epidemiologically explored to account for potential epidemiological biases. The purpose of the present study was to estimate the age-dependent CFR in Tokyo and Osaka over time, investigating the impact of caseload demand on the risk of death.

METHODS

We estimated the time-dependent CFR, accounting for time delay from diagnosis to death. To this end, we first determined the time distribution from diagnosis to death, allowing variations in the delay over time. We then assessed the age-dependent CFR in Tokyo and Osaka. In Osaka, the risk of intensive care unit (ICU) admission was also estimated.

RESULTS

The CFR was highest among individuals aged 80 years and older and during the first epidemic wave from February to June 2020, estimated as 25.4% (95% confidence interval [CI] 21.1 to 29.6) and 27.9% (95% CI 20.6 to 36.1) in Tokyo and Osaka, respectively. During the fourth wave of infection (caused by the Alpha variant) in Osaka the CFR among the 70s and ≥ 80s age groups was, respectively, 2.3 and 1.5 times greater than in Tokyo. Conversely, despite the surge in hospitalizations, the risk of ICU admission among those aged 80 and older in Osaka decreased. Such time-dependent variation in the CFR was not seen among younger patients < 70 years old. With the Omicron variant, the CFR among the 80s and older in Tokyo and Osaka was 3.2% (95% CI 3.0 to 3.5) and 2.9% (95% CI 2.7 to 3.1), respectively.

CONCLUSION

We found that without substantial control, the CFR can increase when a surge in cases occurs with an identifiable elevation in risk-especially among older people. Because active treatment options including admission to ICU cannot be offered to the elderly with an overwhelmed medical service, the CFR value can potentially double compared with that in other areas of health care under less pressure.

A novel method to monitor COVID-19 fatality rate in real-time, a key metric to guide public health policy

An accurate estimator of the real-time fatality rate is warranted to monitor the progress of ongoing epidemics, hence facilitating the policy-making process. However, most of the existing estimators fail to capture the time-varying nature of the fatality rate and are often biased in practice. A simple real-time fatality rate estimator with adjustment for reporting delays is proposed in this paper using the fused lasso technique. This approach is easy to use and can be broadly applied to public health practice as only basic epidemiological data are required. A large-scale simulation study suggests that the proposed estimator is a reliable benchmark for formulating public health policies during an epidemic with high accuracy and sensitivity in capturing the changes in the fatality rate over time, while the other two commonly-used case fatality rate estimators may convey delayed or even misleading signals of the true situation. The application to the COVID-19 data in Germany between January 2020 and January 2022 demonstrates the importance of the social restrictions in the early phase of the pandemic when vaccines were not available, and the beneficial effects of vaccination in suppressing the fatality rate to a low level since August 2021 irrespective of the rebound in infections driven by the more infectious Delta and Omicron variants during the fourth wave.

Novel methods for estimating the instantaneous and overall COVID-19 case fatality risk among care home residents in England

The COVID-19 pandemic has had high mortality rates in the elderly and frail worldwide, particularly in care homes. This is driven by the difficulty of isolating care homes from the wider community, the large population sizes within care facilities (relative to typical households), and the age/frailty of the residents. To quantify the mortality risk posed by disease, the case fatality risk (CFR) is an important tool. This quantifies the proportion of cases that result in death. Throughout the pandemic, CFR amongst care home residents in England has been monitored closely. To estimate CFR, we apply both novel and existing methods to data on deaths in care homes, collected by Public Health England and the Care Quality Commission. We compare these different methods, evaluating their relative strengths and weaknesses. Using these methods, we estimate temporal trends in the instantaneous CFR (at both daily and weekly resolutions) and the overall CFR across the whole of England, and dis-aggregated at regional level. We also investigate how the CFR varies based on age and on the type of care required, dis-aggregating by whether care homes include nursing staff and by age of residents. This work has contributed to the summary of measures used for monitoring the UK epidemic.

During an epidemic, the case fatality risk (CFR), i.e. the probability that an individual dies after testing positive for a disease, is a key parameter informing the public health response. However, calculating the CFR is not trivial, since there are cases who may die in the future but have not died yet. Therefore, statistical methods are required to correct for the distribution of times between testing positive and dying. In this paper, we derive multiple methods, some existing and some novel, within a consistent methodological framework. This allows us to understand how these different approaches are related and their relative strengths and weaknesses. During the COVID-19 pandemic, care homes have been particularly affected, due to the high risk of COVID-19-associated mortality in the frail and elderly. We apply our CFR methods to data from English care homes to analyse changes in the care home CFR throughout the pandemic.

A comparison of two frameworks for multi-state modelling, applied to outcomes after hospital admissions with COVID-19

We compare two multi-state modelling frameworks that can be used to represent dates of events following hospital admission for people infected during an epidemic. The methods are applied to data from people admitted to hospital with COVID-19, to estimate the probability of admission to intensive care unit, the probability of death in hospital for patients before and after intensive care unit admission, the lengths of stay in hospital, and how all these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. We compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, we find that some groups appear to be at very low risk of some events, in particular intensive care unit admission, and these are best represented by using 'cure-rate' models to define transition-specific hazards. We provide general-purpose software to implement all the models we describe in the flexsurv R package, which allows arbitrarily flexible distributions to be used to represent the cause-specific hazards or times to events.

Comparing COVID-19 fatality across countries: a synthetic demographic indicator

Background

The case fatality rate (CFR) is one of the most important measures for monitoring disease progression and evaluating appropriate policy health measures over the course of the COVID-19 pandemic. To remove biases arising from the age structure of COVID-19 cases in international comparisons of the CFR, existing studies have relied mainly on direct standardisation.

Objective

We propose and validate a synthetic indicator of COVID-19 fatality (SCFR) that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases without relying on the arbitrary choice of a standard population.

Results

Contrary to what comparisons of the crude CFR suggest, differences in COVID-19 fatality across countries according to the proposed SCFR are not very stark. Importantly, once we adjust for the age structure of COVID-19 cases, the higher case fatality among men emerges as the main driver of international differences in COVID-19 CFR.

Conclusions

The SCFR is a simple indicator that is useful for monitoring the fatality of SARS-CoV-2 mutations and the efficacy of health policy measures for COVID-19, including vaccination.

Contributions

(1) A simple synthetic indicator of COVID-19 fatality that improves its comparability across countries by adjusting for the age and sex structure of COVID-19 cases; (2) Evidence that sex differences in COVID-19 fatality drive international differences in the overall CFR.

Effect of testing and social distancing measures on COVID-19 deaths in India: Role of pre-existing socio-economic factors

We examine the effect of testing and social distancing measures on the severity of COVID19 across Indian states during the 68th day nationwide lockdown period. We also explore whether pre-existing socio-economic factors such as quality of health care and the ability to practice social distancing influences the effect of these policy measures across states. Using daily level data between April 1 and May 31 for 18 of the major states, we find that both testing and social distancing have a negative effect on COVID-19 fatalities in India. Further, testing is more helpful in reducing CFR for states with lower per capita health expenditure and weaker medical infrastructure. This highlights how ramping up testing can aid states that have a weak health care system through the detection of infection, contact tracing and isolation. In contrast, social distancing measures are more effective in states that are less populous and have lesser people dwelling in single-room houses. Our results confirm the role of pre-existing institutional factors in shaping the effect of policy actions on health outcomes.

The Influence of SARS-CoV-2 Variants on National Case-Fatality Rates: Correlation and Validation Study

Background

In 2021, new variants of the SARS-CoV-2 virus appeared with increased transmissibility and virulence as compared with the original wild variant. The first variants of concern (VoCs), Alpha (B1.1.7) and Gamma (P.1), first appeared in the United Kingdom and Brazil, respectively. The Delta (B.1.617.2) variant, seen in India in October 2020, dominated COVID-19 infections across all regions through the second half of 2021.

Objective

This research explores the degree to which SARS-CoV-2 VoCs generate waves of fluctuations in case-fatality rates (CFRs) across countries in several regions, increase the risk of mortality to persons with certain comorbidities, and decrease the risk of mortality as the percentage of fully vaccinated populations increases.

Methods

This analysis introduces a measure of the temporal dynamics of COVID-19 infections in the form of a proxy CFR (pCFR), which can be compared among countries. It uses economic and demographic data reported by the World Bank and International Monetary Fund, plus publicly available epidemiological and medical statistics reported to the relevant national and international public health authorities. From these ecological data, pandemic average and daily COVID-19 CFRs and their correlations with potential cofactors were computed for 2021, a year dominated by the spread of World Health Organization-designated VoCs. The study does not investigate disease pathology; rather, it compares the daily case rates and pCFRs to reveal underlying contributing factors that vary from country to country and region to region.

Results

The in-depth global regression analysis of cofactors found that the strongest single correlation with COVID-19 fatality was 0.36 (SD 0.02) with P<.001 for chronic kidney disease. No other single physiological cofactors display positive correlations exceeding 0.26 (SD 0.26), with P=.008 (asthma) and P=.01 (coronary disease). The study confirms that the pCFR is a valuable metric for tracking waves of infection due to different VoCs within countries.

Conclusions

The influence of social, economic, and medical cofactors on the CFR due to VoCs remains qualitatively similar, albeit strengthened, to the levels found for the wild strain. The strong regional variations of the influence of all cofactors observed for the wild strain persists in infections for all VoCs with very strong correlation coefficients seen in the Middle East for asthma (0.76), coronary heart disease (0.60), lung disease (0.70), and chronic kidney disease (0.52). Strong regional variations emphasize the influence on COVID-19 mortality due to regional differences in national economics, patterns of health care policies, and variations in cultural practices and environment. The pCFR-based analysis reveals clear patterns of the spread of VoCs across regions, but there is little evidence for the spread of the Lambda and Mu (B.1.621) variants of interest outside of South America.

Optimal control and cost-effective analysis of an age-structured emerging infectious disease model

Emerging infectious diseases are one of the global public health problems which may lead to widespread epidemics and potentially life-threatening infection. Integrated vaccination and physical distancing interventions are two elementary methods for preventing infectious diseases transmission. In this paper, we construct a continuous age-structured model for investigating the transmission dynamics of an emerging infection disease during a short period. We derive the basic regeneration number R0, the spectral radius of the next generation operator K, which determines the disease outbreak or not. Furthermore, we propose an optimal control problem to take account for the cost-effectiveness of social distancing intervention and vaccination. We rigorously obtain sufficient conditions for a L¹ control problem. Numerical simulations show that coupling integrated vaccination and physical distancing intervention could effectively eliminate the infection, and such control strategy is more sensitive for people aged 10–39 and over 60.

Does the Prosperity of a Country Play a Role in COVID-19 Outcomes?

OBJECTIVE

This study aims to clarify the association between prosperity and the coronavirus disease (COVID-19) outcomes and its impact on the future management of pandemics.

METHODS

This is an observational study using information from 2 online registries. The numbers of infected individuals and deaths and the prosperity rank of each country were obtained from worldometer.info and the Legatum Institute's Prosperity Index, respectively.

RESULTS

There is a combination of countries with high and low prosperity on the list of COVID-19-infected countries. The risk of the virus pandemic seems to be more extensive in countries with high prosperity. A Spearman's rho test confirmed a significant correlation between prosperity, the number of COVID-19 cases, and the number of deaths at the 99% level.

CONCLUSION

New emerging pandemics affect all nations. In order to increase the likelihood of successfully managing future events, it is important to consider preexisting health security, valid population-based management approaches, medical decision-making, communication, continuous assessment, triage, treatment, early and complete physical distancing strategies, and logistics. These elements cannot be taught on-site and on occasion. There is a need for innovative and regular educational activities for all stakeholders committed to safeguarding our future defense systems concerning diagnostic, protection, treatment, and rehabilitation in pandemics, as well as other emergencies.

Online Information of COVID-19: Visibility and Characterization of Highest Positioned Websites by Google between March and April 2020-A Cross-Country Analysis

Background: The WHO has used the term “infodemic” to describe the vast amount of false and true information that was making it difficult for people to find reliable information when they needed it. The infodemic spreads faster than COVID-19 itself. The main objective of the study was to characterize and analyze content about COVID-19 returned by Google during the pandemic and compare it between countries. Methods: The study was conducted between 30 March and 27 April 2020. The information was searched through local Google websites using the “COVID-19”, “Coronavirus”, “SARS-CoV-2” and “fake news” keywords. The search was conducted in Australia, France, Germany, Italy, Poland, Singapore, Spain, UK and the USA. The total number of the analyzed webpages was 685. Results: The most frequent types were News websites 47% (324/685) and Governmental 19% (131/685) while the least were Health portals 2% (17/685) and Scientific journals 5% (35/635), p < 0.001. United States and Australia had the highest share of Governmental websites. There was a positive correlation between the amount of preventive information and a number of SARS-CoV-2 infections in countries. The higher the number of tests performed, the higher was the amount of information about prevention available online. Conclusions: Online information is usually available on news and government websites and refers to prevention. There were differences between countries in types of information available online. The highest positioned (the first 20) websites for COVID-19, Coronavirus and SARS-CoV-2 keywords returned by Google include true information.

On the association between COVID-19 vaccination levels and incidence and lethality rates at a regional scale in Spain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19), has led to the deepest global health and economic crisis of the current century. This dramatic situation has forced the public health authorities and pharmaceutical companies to develop anti-COVID-19 vaccines in record time. Currently, almost 80% of the population are vaccinated with the required number of doses in Spain. Thus, in this paper, COVID-19 incidence and lethality rates are analyzed through a segmented spatio-temporal regression model that allows studying if there is an association between a certain vaccination level and a change (in mean) in either the incidence or the lethality rates. Spatial dependency is included by considering the Besag-York-Mollié model, whereas natural cubic splines are used for capturing the temporal structure of the data. Lagged effects between the exposure and the outcome are also taken into account. The results suggest that COVID-19 vaccination has not allowed yet (as of September 2021) to observe a consistent reduction in incidence levels at a regional scale in Spain. In contrast, the lethality rates have displayed a declining tendency which has associated with vaccination levels above 50%.

Data-driven case fatality rate estimation for the primary lineage of SARS-CoV-2 in Poland

After more than one and a half year since the COVID-19 pandemics outbreak the scientific world is constantly trying to understand its dynamics. In this paper of the case fatality rates (CFR) for COVID-19 we study the historic data regarding mortality in Poland during the first six months of pandemic, when no SARS-CoV-2 variants of concern were present among infected. To this end, we apply competing risk models to perform both uni- and multivariate analyses on specific subpopulations selected by different factors including the key indicators: age, sex, hospitalization. The study explores the case fatality rate to find out its decreasing trend in time. Furthermore, we describe the differences in mortality among hospitalized and other cases indicating a sudden increase of mortality among hospitalized cases at the end of the 2020 spring season. Exploratory and multivariate analysis revealed the real impact of each variable and besides the expected factors indicating increased mortality (age, comorbidities) we track more non-obvious indicators. Recent medical care as well as the identification of the source contact, independently of the comorbidities, significantly impact an individual mortality risk. As a result, the study provides a twofold insight into the COVID-19 mortality in Poland. On one hand we explore mortality in different groups with respect to different variables, on the other we indicate novel factors that may be crucial in reducing mortality. The later can be coped, e.g. by more efficient contact tracing and proper organization and management of the health care system to accompany those who need medical care independently of comorbidities or COVID-19 infection.

A sequential test to compare the real-time fatality rates of a disease among multiple groups with an application to COVID-19 data

Infectious diseases, such as the ongoing COVID-19 pandemic, pose a significant threat to public health globally. Fatality rate serves as a key indicator for the effectiveness of potential treatments or interventions. With limited time and understanding of novel emerging epidemics, comparisons of the fatality rates in real-time among different groups, say, divided by treatment, age, or area, have an important role to play in informing public health strategies. We propose a statistical test for the null hypothesis of equal real-time fatality rates across multiple groups during an ongoing epidemic. An elegant property of the proposed test statistic is that it converges to a Brownian motion under the null hypothesis, which allows one to develop a sequential testing approach for rejecting the null hypothesis at the earliest possible time when statistical evidence accumulates. This property is particularly important as scientists and clinicians are competing with time to identify possible treatments or effective interventions to combat the emerging epidemic. The method is widely applicable as it only requires the cumulative number of confirmed cases, deaths, and recoveries. A large-scale simulation study shows that the finite-sample performance of the proposed test is highly satisfactory. The proposed test is applied to compare the difference in disease severity among Wuhan, Hubei province (exclude Wuhan) and mainland China (exclude Hubei) from February to March 2020. The result suggests that the disease severity is potentially associated with the health care resource availability during the early phase of the COVID-19 pandemic in mainland China.

Role of multiple factors likely contributing to severity-mortality of COVID-19

COVID-19 stalled the world in 2020 and continues to be the greatest health crisis of this generation. While the apparent case fatality rates across fluctuates around ~2% globally, associated mortality/death rate (deaths per million population) varies distinctly across regions from the global average of ~600 per million population. Heterogeneous factors have been linked with COVID-19 associated mortalities and these include age, share of geriatric population, comorbidities, trained immunity and climatic conditions. Apart from direct or indirect role of endemic diseases, dietary factors and host immunity in regulating COVID-19 severity, human behaviour will inevitably control outcome of this pandemic. Comprehensive understanding of these factors will have a bearing on management of future health crises.

Using secondary cases to characterize the severity of an emerging or re-emerging infection

The methods to ascertain cases of an emerging infectious disease are typically biased toward cases with more severe disease, which can bias the average infection-severity profile. Here, we conducted a systematic review to extract information on disease severity among index cases and secondary cases identified by contact tracing of index cases for COVID-19. We identified 38 studies to extract information on measures of clinical severity. The proportion of index cases with fever was 43% higher than for secondary cases. The proportion of symptomatic, hospitalized, and fatal illnesses among index cases were 12%, 126%, and 179% higher than for secondary cases, respectively. We developed a statistical model to utilize the severity difference, and estimate 55% of index cases were missed in Wuhan, China. Information on disease severity in secondary cases should be less susceptible to ascertainment bias and could inform estimates of disease severity and the proportion of missed index cases.

COVID-19: Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques

BACKGROUND AND OBJECTIVES

Epidemiological models of epidemic spread are an essential tool for optimizing decision-making. The current literature is very extensive and covers a wide variety of deterministic and stochastic models. However, with the increase in computing resources, new, more general, and flexible procedures based on simulation models can assess the effectiveness of measures and quantify the current state of the epidemic. This paper illustrates the potential of this approach to build a new dynamic probabilistic model to estimate the prevalence of SARS-CoV-2 infections in different compartments.

METHODS

We propose a new probabilistic model in which, for the first time in the epidemic literature, parameter learning is carried out using gradient-free stochastic black-box optimization techniques simulating multiple trajectories of the infection dynamics in a general way, solving an inverse problem that is defined employing the daily information from mortality records.

RESULTS

After the application of the new proposal in Spain in the first and successive waves, the result of the model confirms the accuracy to estimate the seroprevalence and allows us to know the real dynamics of the pandemic a posteriori to assess the impact of epidemiological measures by the Spanish government and to plan more efficiently the subsequent decisions with the prior knowledge obtained.

CONCLUSIONS

The model results allow us to estimate the daily patterns of COVID-19 infections in Spain retrospectively and examine the population's exposure to the virus dynamically in contrast to seroprevalence surveys. Furthermore, given the flexibility of our simulation framework, we can model situations -even using non-parametric distributions between the different compartments in the model- that other models in the existing literature cannot. Our general optimization strategy remains valid in these cases, and we can easily create other non-standard simulation epidemic models that incorporate more complex and dynamic structures.

COVID-19-associated mortality across the countries of the Gulf Cooperation Council and how it compares to Europe: A comparative study

INTRODUCTION

In late 2019, a novel strain of coronavirus, discovered in the city of Wuhan, China, was found to cause a disease later named coronavirus disease 2019, or COVID-19. In January 2020, COVID-19 first reached the Gulf region. Afterwards, the disease spread rapidly across the countries of the Gulf and the number of COVID-19 cases rose significantly. Now, more than a year later, there are only a limited number of studies regarding COVID-19 and its behavior in this region. In this article, we aim to assess the mortality caused by the disease in the Gulf region by calculating the Case Fatality Rates (CFR) for all of the Gulf Cooperation countries and comparing the results with those of Europe.

METHODS

Data was obtained from the official statistics of the World Health Organization (WHO) from January to May 2020. From the data, the CFR was calculated for every Gulf and European country included in the study. Following the calculation, the results were compared and analyzed. To make our comparison more accurate, we added the total number of COVID-19 tests per 1000 population and the Health Access and Quality index for each individual country.

RESULTS

CFRs in the Gulf region to May 12, 2020 were: United Arab Emirates (1.06%), Kuwait (0.69%), Saudi Arabia (0.62%), Oman (0.45%), Bahrain (0.15%), and Qatar (0.06%). Within Europe over the same time period, 10 countries had CFRs above 10%, with the majority above 3%.

CONCLUSIONS

Compared to Europe, the COVID-19 mortality rate in the Gulf region has been much lower. The difference in age groups between the Gulf region and Europe may be the most important factor, mainly due to a younger population and a smaller elderly demographic in the Gulf region. Although age is a strong factor for the lower CFR in the Gulf, other factors must also be considered. These include the number of COVID-19 tests conducted per population, different country capabilities, and varying criteria for reporting COVID-19 deaths(Table-1)(Table-2).

Polymeric Materials as Potential Inhibitors Against SARS-CoV-2

Recently discovered SARS-CoV-2 caused a pandemic that triggered researchers worldwide to focus their research on all aspects of this new peril to humanity. However, in the absence of specific therapeutic intervention, some preventive strategies and supportive treatment minimize the viral transmission as studied by some factors such as basic reproduction number, case fatality rate, and incubation period in the epidemiology of viral diseases. This review briefly discusses coronaviruses' life cycle of SARS-CoV-2 in a human host cell and preventive strategies at some selected source of infection. The antiviral activities of synthetic and natural polymers such as chitosan, hydrophobically modified chitosan, galactosylated chitosan, amine-based dendrimers, cyclodextrin, carrageenans, polyethyleneimine, nanoparticles are highlighted in this article. Mechanism of virus inhibition, detection and diagnosis are also presented. It also suggests that polymeric materials and nanoparticles can be effective as potential inhibitors and immunization against coronaviruses which would further develop new technologies in the field of polymer and nanoscience.

Voting in a global pandemic: Assessing dueling influences of Covid-19 on turnout

OBJECTIVE

We investigate the impact of a global health crisis on political behavior. Specifically, we assess the impact of Covid-19 incidence rates, and the impact of temporal and spatial proximity to the crisis, on voter turnout in the 2020 Brazilian municipal elections.

METHODS

We use Ordinary Least Squares and Spatial Durbin Error models to evaluate sub-national variation in municipal-level Covid-19 incidence and voter turnout. We include controls for political, economic, health, and state context.

RESULTS

Ceteris paribus, increasing deaths in the month leading up to the election from 0.01 to 1 per 1000 people is associated with a 5 percentage point decrease in turnout; higher cases and deaths earlier in the pandemic are generally associated with higher turnout. Covid-19 incidence rates in nearby municipalities affect local turnout in the same directions.

CONCLUSION

Higher Covid-19 incidence near the time of the election decreases voter turnout, while incidence farther from the election increases voter turnout.

Underdetection in a stochastic SIR model for the analysis of the COVID-19 Italian epidemic

We propose a way to model the underdetection of infected and removed individuals in a compartmental model for estimating the COVID-19 epidemic. The proposed approach is demonstrated on a stochastic SIR model, specified as a system of stochastic differential equations, to analyse data from the Italian COVID-19 epidemic. We find that a correct assessment of the amount of underdetection is important to obtain reliable estimates of the critical model parameters. The adaptation of the model in each time interval between relevant government decrees implementing contagion mitigation measures provides short-term predictions and a continuously updated assessment of the basic reproduction number.

Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values

BACKGROUND

Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR.

METHODS

We calculated age specific (0-69, 70-79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time.

RESULTS

The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic.

CONCLUSIONS

We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.

On the management of COVID-19 pandemic in Italy

The fast-moving coronavirus disease 2019 (COVID-19) called for a rapid response to slowing down the viral spread and reduce the fatality associated to the pandemic. Policymakers have implemented a wide range of non-pharmaceutical interventions to mitigate the spread of the pandemic and reduce burdens on healthcare systems. An efficient response of healthcare systems is crucial to handle a health crisis. Understanding how non-pharmaceutical interventions have contributed to slowing down contagions and how healthcare systems have impacted on fatality associated with health crisis is of utmost importance to learn from the COVID-19 pandemic. We investigated these dynamics in Italy at the regional level. We found that the simultaneous introduction of a variety of measures to increase social distance is associated with an important decrease in the number of new infected patients detected daily. Contagion reduces by 1% with the introduction of lockdowns in an increasing number of regions. We also found that a robust healthcare system is crucial for containing fatality associated with COVID-19. Also, proper diagnosis strategies are determinant to mitigate the severity of the health outcomes. The preparedness is the only way to successfully adopt efficient measures in response of unexpected emerging pandemics.

Estimating the case fatality ratio for COVID-19 using a time-shifted distribution analysis

Estimating the case fatality ratio (CFR) for COVID-19 is an important aspect of public health. However, calculating CFR accurately is problematic early in a novel disease outbreak, due to uncertainties regarding the time course of disease and difficulties in diagnosis and reporting of cases. In this work, we present a simple method for calculating the CFR using only public case and death data over time by exploiting the correspondence between the time distributions of cases and deaths. The time-shifted distribution (TSD) analysis generates two parameters of interest: the delay time between reporting of cases and deaths and the CFR. These parameters converge reliably over time once the exponential growth phase has finished. Analysis is performed for early COVID-19 outbreaks in many countries, and we discuss corrections to CFR values using excess-death and seroprevalence data to estimate the infection fatality ratio (IFR). While CFR values range from 0.2% to 20% in different countries, estimates for IFR are mostly around 0.5-0.8% for countries that experienced moderate outbreaks and 1-3% for severe outbreaks. The simplicity and transparency of TSD analysis enhance its usefulness in characterizing a new disease as well as the state of the health and reporting systems.

Incidence and outcome of SARS-CoV-2 infection on solid organ transplantation recipients: A nationwide population-based study

Since February 21 2020, when the Italian National Institute of Health (Istituto Superiore di Sanità-ISS) reported the first autochthonous case of infection, a dedicated surveillance system for SARS-CoV-2-positive (COVID+) cases has been created in Italy. These data were cross-referenced with those inside the Information Transplant System in order to assess the cumulative incidence (CI) and the outcome of SARS-COV-2 infection in solid organ transplant recipients (SOTRs) who are assumed to be most at risk. We compared our results with those of COVID+ nontransplanted patients (Non-SOTRs) with follow-up through September 30, 2020. The CI of SARS-CoV-2 infection in SOTRs was 1.02%, higher than in COVID+ Non-SOTRs (0.4%, p < .05) with a greater risk in the Lombardy region (2.89%). The CI by type of organ transplant was higher for heart (CI 1.57%, incidence rate ratio [IRR] 1.36) and lower for liver (CI 0.63%, IRR 0.54). The 60-day CI of mortality was 30.6%, twice as much that of COVID+ Non-SOTRs (15.4%) with a 60-day gender and age adjusted odds ratio (adjusted-OR) of 3.83 for COVID+ SOTRs (95% confidence interval [3.03-4.85]). The lowest 60-day adjusted-OR was observed in liver SOTRs (OR 0.46, 95% confidence interval [0.25-0.86]). More detailed studies on disease management and evolution will be necessary in these patients at greater risk of COVID-19.

Acuity level of care as a predictor of case fatality and prolonged hospital stay in patients with COVID-19: a hospital-based observational follow-up study from Pakistan

OBJECTIVES

To determine if there is an association between acuity level of care (ALC), case fatality and length of stay in patients admitted to hospital due to COVID-19.

DESIGN

A hospital-based observational follow-up study.

SETTING

Internal Medicine Service of the Aga Khan University Hospital, Pakistan, from 26 February 2020 to 30 June 2020.

PARTICIPANTS

Adult patients with confirmed COVID-19, aged ≥18 years.

METHODS

ALC was categorised into low, intermediate and high level and patients were triaged using the standard emergency severity illness score. All patients were followed until the end of hospital admission for the outcome of case fatality and length of stay.

RESULTS

A total of 822 patients with COVID-19 were admitted during the study period and 699 met inclusion criteria. The mean age was 54.5 years and 67% were males; 50.4% were triaged to low, 42.5% to intermediate and 7.2% to high acuity care. The overall case-fatality rate was 11.6%, with the highest (52%) in high acuity level followed by 16.2% in intermediate and 2% in low acuity care. Acuity level was associated with case fatality, with an HR (95% CI) of 5.0 (2.0 to 12.1) for high versus low acuity care and an HR of 2.7 (1.2, 6.4) for intermediate versus low acuity care, after adjusting for age, sex and common comorbidities including diabetes, hypertension, ischaemic heart disease and chronic lung disease. Similarly, acuity level was also associated with length of hospital stay.

CONCLUSION

High and intermediate acuity level is associated with higher case fatality rate and prolonged length of hospital stay in patients admitted with COVID-19. In resource-limited settings where the provision of high acuity care is limited, the intermediate care acuity could serve as a useful strategy to treat relatively less critical patients with COVID-19.

Analysis of the time evolution of COVID-19 lethality during the first epidemic wave in Italy

BACKGROUND AND AIM

While the entire world is still experiencing the dramatic emergency due to SARS-CoV-2, Italy has a prominent position since it has been the locus of the first major outbreak among Western countries. The aim of this study is the evaluation of temporal connection between SARS-CoV-2 positive tests (cases) and deaths in Italy in the first wave of the epidemic.

METHODS

A temporal link between cases and deaths was determined by comparing their daily/weekly trends using surveillance data of the period March 2-June 2020.

RESULTS

The monitoring of the cases/deaths evolution during the first wave of the outbreak highlights a striking correlation between infections of a certain week and deaths of the following one. We defined a weekly lethality rate that is virtually unchanged over the entire months of April and May until the first week of June (≈13.6%). Due to the rather low number of cases/deaths, this parameter starts to fluctuate in the following three weeks.

CONCLUSIONS

The analysis indicates that the weekly lethality rate is virtually unchanged over the entire first wave of the epidemic, despite the progressive increase of the testing. As observed for the overall lethality, this parameter uniformly presents rather high values. The definition of a temporal link between cases and deaths will likely represent a useful tool for highlighting analogies and differences between the first and the second wave of the pandemic and for evaluating the effectiveness, even if partial, of the strategies applied during the ongoing outbreak. (www.actabiomedica.it).

Evaluating alternative hypotheses to explain the downward trend in the indices of the COVID-19 pandemic death rate

BACKGROUND

In the ongoing Covid-19 pandemic, in the global data on the case fatality ratio (CFR) and other indices reflecting death rate, there is a consistent downward trend from mid-April to mid-November. The downward trend can be an illusion caused by biases and limitations of data or it could faithfully reflect a declining death rate. A variety of explanations for this trend are possible, but a systematic analysis of the testable predictions of the alternative hypotheses has not yet been attempted.

METHODOLOGY

We state six testable alternative hypotheses, analyze their testable predictions using public domain data and evaluate their relative contributions to the downward trend.

RESULTS

We show that a decline in the death rate is real; changing age structure of the infected population and evolution of the virus towards reduced virulence are the most supported hypotheses and together contribute to major part of the trend. The testable predictions from other explanations including altered testing efficiency, time lag, improved treatment protocols and herd immunity are not consistently supported, or do not appear to make a major contribution to this trend although they may influence some other patterns of the epidemic.

CONCLUSION

The fatality of the infection showed a robust declining time trend between mid April to mid November. Changing age class of the infected and decreasing virulence of the pathogen were found to be the strongest contributors to the trend.

Asia's largest urban slum-Dharavi: A global model for management of COVID-19

The Dharavi sector of Mumbai, India is constituted as one of the biggest slums of Asia and the world's most populated areas. The COVID-19 outburst is at its peak in May and curve gets flattened within next two months. This article focuses on factors responsible for transmission, prevention and in controlling the COVID-19 spread in Dharavi. The COVID-19 data was interpreted in terms of Case Recovery Rate (CRR) and Case Fatality Rate (CFR) for total and closed both. The CFR per closed reached 10.12% nearly to per total 9.82% in Dharavi on 31st July 2020. The CRR per total cases in Dharavi and Mumbai is 87.16 & 76.18% and per closed cases is 89.87 & 93.20% respectively in comparison to the CFR. The calculated CFR and CRR for both Dharavi and Mumbai suggest that the COVID-19 recovery rate is more than the fatality rate. The Dharavi people serve as an important role in controlling the pandemic by adopting 4-T model tracing, tracking, testing, and treating strategy. This article is beneficial for government policy makers and private agencies in controlling the COVID-19 pandemic in densely populated areas and also for nations with high fatality rate.

Bertalanffy-Pütter models for the first wave of the COVID-19 outbreak

The COVID-19 pandemics challenges governments across the world. To develop adequate responses, they need accurate models for the spread of the disease. Using least squares, we fitted Bertalanffy-Pütter (BP) trend curves to data about the first wave of the COVID-19 pandemic of 2020 from 49 countries and provinces where the peak of the first wave had been passed. BP-models achieved excellent fits (R-squared above 99%) to all data. Using them to smoothen the data, in the median one could forecast that the final count (asymptotic limit) of infections and fatalities would be 2.48 times (95% confidence limits 2.42-2.6) and 2.67 times (2.39-2.765) the total count at the respective peak (inflection point). By comparison, using logistic growth would evaluate this ratio as 2.00 for all data. The case fatality rate, defined as the quotient of the asymptotic limits of fatalities and confirmed infections, was in the median 4.85% (confidence limits 4.4%-6.5%). Our result supports the strategies of governments that kept the epidemic peak low, as then in the median fewer infections and fewer fatalities could be expected.

COVID-19 fatality rates in hospitalized patients: systematic review and meta-analysis

Background

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Although general and local public health report deathly cases, case fatality rates are still largely unknown. Thus, we sought to evaluate the mortality of COVID-19.

Methods

We searched PubMed and EMBASE databases for articles evaluating the clinical characteristics of COVID-19 patients that included clinical outcomes, between December 2020 and 24 April 2020. Two authors performed an independent selection using predefined terms of search.

Results

We retrieved 33 studies with a total of 13,398 patients with COVID-19 diagnosis. The mortality rate of the COVID‐19 patients was 17.1% (95% CI 12.7; 22.7, I² = 96.9%). For general patients admitted to the hospital (excluding critical care-only studies) the mortality rate of the COVID‐19 was 11.5% (95% CI 7.7; 16.9, I² = 96.7%). Among critical illness studies (n = 7) we found a 40.5% mortality (95% CI 31.2; 50.6, I² = 91.8%).

Conclusion

High COVID-19 mortality among general admitted patients and critical care cases should guide resources allocations and economic burden calculations during the pandemics.

Estimation of the case fatality rate based on stratification for the COVID-19 outbreak

This work is motivated by the recent worldwide pandemic of the novel coronavirus disease (COVID-19). When an epidemiological disease is prevalent, estimating the case fatality rate, the proportion of deaths out of the total cases, accurately and quickly is important as the case fatality rate is one of the crucial indicators of the risk of a disease. In this work, we propose an alternative estimator of the case fatality rate that provides more accurate estimate during an outbreak by reducing the downward bias (underestimation) of the naive CFR, the proportion of deaths out of confirmed cases at each time point, which is the most commonly used estimator due to the simplicity. The proposed estimator is designed to achieve the availability of real-time update by using the commonly reported quantities, the numbers of confirmed, cured, deceased cases, in the computation. To enhance the accuracy, the proposed estimator adapts a stratification, which allows the estimator to use information from heterogeneous strata separately. By the COVID-19 cases of China, South Korea and the United States, we numerically show the proposed stratification-based estimator plays a role of providing an early warning about the severity of a epidemiological disease that estimates the final case fatality rate accurately and shows faster convergence to the final case fatality rate.

COVID-19 case fatality risk by age and gender in a high testing setting in Latin America: Chile, March-August 2020

BACKGROUND

Early severity estimates of coronavirus disease 2019 (COVID-19) are critically needed to assess the potential impact of the ongoing pandemic in different demographic groups. Here we estimate the real-time delay-adjusted case fatality rate across nine age groups by gender in Chile, the country with the highest testing rate for COVID-19 in Latin America.

METHODS

We used a publicly available real-time daily series of age-stratified COVID-19 cases and deaths reported by the Ministry of Health in Chile from the beginning of the epidemic in March through August 31, 2020. We used a robust likelihood function and a delay distribution to estimate real-time delay-adjusted case-fatality risk and estimate model parameters using a Monte Carlo Markov Chain in a Bayesian framework.

RESULTS

As of August 31, 2020, our estimates of the time-delay adjusted case fatality rate (CFR) for men and women are 4.16% [95% Credible Interval (CrI): 4.09-4.24%] and 3.26% (95% CrI: 3.19-3.34%), respectively, while the overall estimate is 3.72% (95% CrI: 3.67-3.78%). Seniors aged 80 years and over have an adjusted CFR of 56.82% (95% CrI: 55.25-58.34%) for men and 41.10% (95% CrI: 40.02-42.26%) for women. Results showed a peak in estimated CFR during the June peak of the epidemic. The peak possibly reflects insufficient laboratory capacity, as illustrated by high test positivity rates (33% positive 7-day average nationally in June), which may have resulted in lower reporting rates.

CONCLUSIONS

Severity estimates from COVID-19 in Chile suggest that male seniors, especially among those aged ≥ 70 years, are being disproportionately affected by the pandemic, a finding consistent with other regions. The ongoing pandemic is imposing a high death toll in South America, and Chile has one of the highest reported mortality rates globally thus far. These real-time estimates may help inform public health officials' decisions in the region and underscore the need to implement more effective measures to ameliorate fatality.

The Burden of Malnutrition and Fatal COVID-19: A Global Burden of Disease Analysis

Background: Although reasonable to assume, it is not yet clear whether malnourished countries are at higher risk for severe or fatal coronavirus disease 2019 (COVID-19). This study aims to identify the countries where prevalent malnutrition may be a driving factor for fatal disease after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Methods: Using estimates from the Global Burden of Disease 2019, country-level burden of malnutrition was quantified using four indicators: death rates for child growth failure (underweight, stunting, and/or wasting) and years lived with disability (YLD) attributed to iron and vitamin A deficiencies and high body mass index (BMI). Global mortality descriptors of the ongoing COVID-19 pandemic were extracted from the European Centre for Disease Prevention and Control, and case fatality ratios (CFRs) were calculated introducing a lag time of 10 weeks after the first death of a confirmed case. Bivariate analyses for 172 countries were carried out for malnutrition indicators and fatal COVID-19. Correlations between burden indicators were characterized by Spearman's rank correlation coefficients (ρ) and visually by scatterplots. Restricted cubic splines and underlying negative binomial regressions adjusted for countries' age-structure, prevalent chronic comorbidities related to COVID-19, population density, and income group were used to explore non-linear relationships. Results: Stratified by the World Bank income group, a moderate positive association between YLD rates for iron deficiency and CFRs for COVID-19 was observed for low-income countries (ρ = 0.60, p = 0.027), whereas no clear indications for the association with child growth failure, vitamin A deficiency, or high BMI were found (ρ < 0.30). Countries ranking high on at least three malnutrition indicators and presenting also an elevated CFR for COVID-19 are sub-Saharan African countries, namely, Angola, Burkina Faso, Chad, Liberia, Mali, Niger, Sudan, and Tanzania, as well as Yemen and Guyana. Conclusions: Population-level malnutrition appears to be related to increased rates of fatal COVID-19 in areas with an elevated burden of undernutrition, such as countries in the Sahel strip. COVID-19 response plans in malnourished countries, vulnerable to fatal COVID-19, should incorporate food security, nutrition, and social protection as a priority component in order to reduce COVID-19 fatality.

An epidemic model integrating direct and fomite transmission as well as household structure applied to COVID-19

This paper stresses its base contribution on a new SIR-type model including direct and fomite transmission as well as the effect of distinct household structures. The model derivation is modulated by several mechanistic processes inherent from typical airborne diseases. The notion of minimum contact radius is included in the direct transmission, facilitating the arguments on physical distancing. As fomite transmission heavily relates to former-trace of sneezes, the vector field of the system naturally contains an integral kernel with time delay indicating the contribution of undetected and non-quarantined asymptomatic cases in accumulating the historical contamination of surfaces. We then increase the complexity by including the different transmission routines within and between households. For airborne diseases, within-household interactions play a significant role in the propagation of the disease rendering countrywide effect. Two steps were taken to include the effect of household structure. The first step subdivides the entire compartments (susceptible, exposed, asymptomatic, symptomatic, recovered, death) into the household level and different infection rates for the direct transmission within and between households were distinguished. Under predefined conditions and assumptions, the governing system on household level can be raised to the community level. The second step then raises the governing system to the country level, where the final state variables estimate the total individuals from all compartments in the country. Two key attributes related to the household structure (number of local households and number of household members) effectively classify countries to be of low or high risk in terms of effective disease propagation. The basic reproductive number is calculated and its biological meaning is invoked properly. The numerical methods for solving the DIDE-system and the parameter estimation problem were mentioned. Our optimal model solutions are in quite good agreement with datasets of COVID-19 active cases and related deaths from Germany and Sri Lanka in early infection, allowing us to hypothesize several unobservable situations in the two countries. Focusing on extending minimum contact radius and reducing the intensity of individual activities, we were able to synthesize the key parameters telling what to practice.

SARS-CoV-2 Infections and COVID-19 Fatality: Estimation of Infection Fatality Ratio and Current Prevalence

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).

Comprehensive public health evaluation of lockdown as a non-pharmaceutical intervention on COVID-19 spread in India: national trends masking state-level variations

OBJECTIVES

To evaluate the effect of four-phase national lockdown from March 25 to May 31 in response to the COVID-19 pandemic in India and unmask the state-wise variations in terms of multiple public health metrics.

DESIGN

Cohort study (daily time series of case counts).

SETTING

Observational and population based.

PARTICIPANTS

Confirmed COVID-19 cases nationally and across 20 states that accounted for >99% of the current cumulative case counts in India until 31 May 2020.

EXPOSURE

Lockdown (non-medical intervention).

MAIN OUTCOMES AND MEASURES

We illustrate the masking of state-level trends and highlight the variations across states by presenting evaluative evidence on some aspects of the COVID-19 outbreak: case fatality rates, doubling times of cases, effective reproduction numbers and the scale of testing.

RESULTS

The estimated effective reproduction number R for India was 3.36 (95% CI 3.03 to 3.71) on 24 March, whereas the average of estimates from 25 May to 31 May stands at 1.27 (95% CI 1.26 to 1.28). Similarly, the estimated doubling time across India was at 3.56 days on 24 March, and the past 7-day average for the same on 31 May is 14.37 days. The average daily number of tests increased from 1717 (19-25 March) to 113 372 (25-31 May) while the test positivity rate increased from 2.1% to 4.2%, respectively. However, various states exhibit substantial departures from these national patterns.

CONCLUSIONS

Patterns of change over lockdown periods indicate the lockdown has been partly effective in slowing the spread of the virus nationally. However, there exist large state-level variations and identifying these variations can help in both understanding the dynamics of the pandemic and formulating effective public health interventions. Our framework offers a holistic assessment of the pandemic across Indian states and union territories along with a set of interactive visualisation tools that are daily updated at covind19.org.

Estimating the Risk of Death from COVID-19 in Adult Cancer Patients

AIMS

During the coronavirus disease 2019 (COVID-19) pandemic, organisations have produced management guidance for cancer patients and the delivery of cytotoxic chemotherapy, but none has offered estimates of risk or the potential impact across populations.

MATERIALS AND METHODS

We combined data from four countries to produce pooled age-banded case fatality rates, calculated the sex difference in survival and used data from four recent studies to convert case fatality rates into age/sex-stratified infection fatality rates (IFRs). We estimated the additional risk of death in cancer patients and in those receiving chemotherapy. We illustrate the impact of these by considering the impact on a national incident cancer cohort and analyse the risk-benefit in some clinical scenarios.

RESULTS

We obtained data based on 412 985 cases and 41 854 deaths. The pooled estimate for IFR was 0.92%. IFRs for patients with cancer ranged from 0 to 29% and were higher in patients receiving chemotherapy (0.01-46%). The risk was significantly higher with age and in men compared with women. 37.5% of patients with a new diagnosis of cancer in 2018 had an IFR ≥5%. Survival benefits from adjuvant chemotherapy ranged from 5 to 10% in some common cancers, compared with the increased risk of death from COVID-19 of 0-3%.

CONCLUSIONS

Older male patients are at a higher risk of death with COVID-19. Patients with cancer are also at a higher risk, as are those who have recently received chemotherapy. We provide well-founded estimates to allow patients and clinicians to better balance these risks and illustrate the wider impact in a national incident cohort.

Epidemic Trend of COVID-19 Transmission in India During Lockdown-1 Phase

To assess the current epidemic trend of COVID-19/SARS-CoV-2 in India, the epidemic dynamics of COVID-19 cases in India in terms of Case Fatality Rate (CFR), Case Recovery Rate (CRR) and Mortality rate (MR) COVID-19 have been evaluated during Lockdown-1. The analysis includes (i) epidemic curve of Covid-19 cases (ii) demographic analysis (iii) calculation of the CFR and CRR by different methods (iv) calculation of MR (v) Geo-temporal analysis (vi) epidemiological transmission factor (vii) evaluation of the effects and impact of infection, prevention and control in India. A total of 10,815 COVID-19 confirmed cases have been reported in 31 states/union territories as of April 14, 2020 with 9272 active cases (85.73%), 1190 cured/discharged (11%), and 353 deaths (3.23%). Among confirmed cases, most cases (59%) are aged 20-49 which is working age in India and 76% cases are reported for men. The median age of Indian COVID-19 patients found to be 39. As of April 14, the CFR per total cases in India is 3.32% and per closed cases is 23.27%. The CRR per total cases in India is 11.00% and per closed cases is 76.72%, which indicates that the recovery rate of COVID-19 is more than the fatality rate in India. The prevention and control measures taken by the state and central governments at all levels and measure of maintenance of social distancing by people have resulted in effective curbing in the COVID-19 transmission in India.

Analysis of the Fatality Rate in Relation to Testing Capacity during the First 50 days of the COVID-19 Epidemic in Italy

Italy has been one of the most severely affected countries by the COVID-19 pandemic, and the case fatality rate (CFR) estimated based on Italian data is one of the highest worldwide. We analyzed public data from the first 50 days of the epidemic in Italy (from February 24 to April 13, 2020) to evaluate whether evolving testing strategies and capacity could account for trends in the CFR. The CFR increased during the study period, and a significant positive correlation was found between the CFR and the percentage of positive tests among performed real-time PCR tests (positive tests % [POS%]) until March 25, suggesting the surveillance system did not detect a growing number of cases in the initial phase of the epidemic. To avoid distortion due to the delay between the identification of cases and deaths, the expected CFR (expCFR) was calculated, which represents the ratio between the predicted number of cases and deaths at the end of the epidemic based on the best fitting logistic curves of the cumulative numbers of cases and deaths. The expCFR began a downward trend from the 40th day. In the final phase, a decrease in both expCFR and POS% was identified, suggesting an improvement in surveillance. The results of this study suggest data from the first 50 days of the COVID-19 epidemic in Italy were severely affected by ascertainment bias. Insufficient testing and isolation of cases could have facilitated the widespread transmission of COVID-19 in the early stages of the outbreak.

The age distribution of mortality from novel coronavirus disease (COVID-19) suggests no large difference of susceptibility by age

Among Italy, Spain, and Japan, the age distributions of COVID-19 mortality show only small variation even though the number of deaths per country shows large variation. To understand the determinant for this situation, we constructed a mathematical model describing the transmission dynamics and natural history of COVID-19 and analyzed the dataset of mortality in Italy, Spain, and Japan. We estimated the parameter which describes the age-dependency of susceptibility by fitting the model to reported data, including the effect of change in contact patterns during the epidemics of COVID-19, and the fraction of symptomatic infections. Our study revealed that if the mortality rate or the fraction of symptomatic infections among all COVID-19 cases does not depend on age, then unrealistically different age-dependencies of susceptibilities against COVID-19 infections between Italy, Japan, and Spain are required to explain the similar age distribution of mortality but different basic reproduction numbers (R₀). Variation of susceptibility by age itself cannot explain the robust age distribution in mortality by COVID-19 infections in those three countries, however it does suggest that the age-dependencies of (i) the mortality rate and (ii) the fraction of symptomatic infections among all COVID-19 cases determine the age distribution of mortality by COVID-19.