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. 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 non-pharmaceutical interventions 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.

Estimation of the Lethality Rate, Recovery Rate, and Case Fatality Ratio of Classical Swine Fever in Japanese Wild Boar: An Analysis of the Epidemics From September 2018 to March 2019

Understanding the morbidity and lethality of diseases is necessary to evaluate the effectiveness of countermeasure against the epidemics (e.g., vaccination). To estimate them, detailed data on host population dynamics are required; however, estimating the population size for wildlife is often difficult. We aimed to elucidate the morbidity and lethality of classical swine fever (CSF) currently highly prevalent in the wild boar population in Japan. To this end, we estimated lethality rate, recovery rate, and case fatality ratio (CFR) of CSF without detailed data on the population estimates of wild boar. A mathematical model was constructed to describe the CSF dynamics and population dynamics of wild boar. We fitted the model to the (i) results of the reverse transcription polymerase chain reaction (RT-PCR) test for the CSFV gene and the (ii) results of the enzyme-linked immunosorbent assay (ELISA) test for the antibody against CSFV in sampled wild boar. In the 280 wild boar sampled from September 2018 to March 2019 in the major CSF-affected area in Japan, the lethality rate and recovery rate of CSF per week were estimated as 0.165 (95% confidence interval: 0.081–0.250) and 0.004 (0–0.009), respectively. While the estimate of lethality rate of CSF was similar with the estimates in previous studies, the recovery rate was lower than those reported previously. CFR was estimated as 0.959 (0.904–0.981) using our estimate of recovery rate. This study is the first to estimate lethality rate of CSF from the dynamics of CSF epidemics in the wild boar population. Since the value of CFR is sensitive to the value of recovery rate, the accuracy in the estimate of recovery rate is a key for the accurate estimation of CFR. A long-term transmission experiment of moderately virulent strains may lead to more accurate estimation of the recovery rate and CFR of CSF.

Analysis of Fatality of Cases by the New Coronavirus, in the Mexican State of Guanajuato: A Cross-sectional Study

Aims: COVID-19 has been a big public health challenge around the world in the past several months. The aim of this study is to describe the epidemic and report of fatality of confirmed cases of COVID-19 in the Mexican state of Guanajuato, until October 2, 2020. Study Design: Cross-sectional, quantitative, analytical study. Place and Duration of Study: Registries of confirmed cases for COVID-19 in Mexican population from January until October 2, 2020, from National System of Epidemiological Surveillance/ General Direction of Epidemiology/ Secretary of Health, Mexico. Methodology: Based on the National Epidemiological Surveillance System Database from Mexico was used in this study. Data were collected on age, sex, comorbidities (i.e., diabetes, chronic obstructive pulmonary disease, asthma, hypertension, cardiovascular disease, immunosuppression, chronic kidney disease, obesity, and smoking), date of death, and real-time reverse transcription polymerase test results. Statistical analyses used were Case Fatality Ratio, Chi- squared test and P-value to show relationships among variables. Odds Ratio and confidence intervals at 95% were reported to show the effect of comorbidities on death due to COVID-19. Also, a Bayesian network model was fitted to assess the statistical dependence among risk factors, comorbidities, and death. Results: There were 100,109 suspected cases, of which 41.69% were positive for SARS-CoV-2. Being older than 60 and male had a higher effect on fatality by COVID-19. In Guanajuato state, 1,457 (48.68%) of deaths occurred in Mexican Institute of Social Security, with a case fatality ratio of 15.63%; meanwhile, in the Ministry of Health from Guanajuato State occurred 1,260 (42.10%) of the deaths with a case fatality ratio of 4.14%. Diabetes (OR 5.16, CI95% 4.77–5.59), chronic obstructive pulmonary disease (OR 6.34, CI95% 5.37–7.49), immunosuppression (OR 2.85, CI95% 2.17–3.76), cardiovascular disease (OR 4.20, CI95% 3.51–5.02), hypertension (OR 4.74, CI95% 4.39–5.11), chronic kidney disease (OR 6.27, CI95% 5.30–7.42), obesity (OR 1.87, CI95% 1.72–2.03), and smoking (OR 1.60, CI95% 1.41–1.81) had effect on death by COVID-19. Asthma had a preventive effect on death (OR=0.72, CI95% 0.54–0.97), but this effect is diluted after adjusting by sex and age. In all cases, age and sex, acted as confounder. Conclusion: Among the Guanajuato population with COVID-19, the main risk factor for dying were age and sex. However, diabetes, chronic obstructive pulmonary disease, immunosuppression, cardiovascular disease, chronic kidney disease, obesity, and smoking are risk factors for dying. Although, comorbidities and risk factors are highly correlated. HIV/AIDS has no effect on fatality from SARS-CoV-2 disease and whereas asthma shows to be a protective factor.

Predicting death from COVID-19 using pre-existing conditions: implications for vaccination triage

IntroductionGlobal shortages in the supply of SARS-CoV-2 vaccines have resulted in campaigns to first inoculate individuals at highest risk for death from COVID-19. Here, we develop a predictive model of COVID-19-related death using longitudinal clinical data from patients in metropolitan Detroit.MethodsAll individuals included in the analysis had a laboratory-confirmed SARS-CoV-2 infection. Thirty-six pre-existing conditions with a false discovery rate p<0.05 were combined with other demographic variables to develop a parsimonious prediction model using least absolute shrinkage and selection operator regression. The model was then prospectively validated in a separate set of individuals with confirmed COVID-19.ResultsThe study population consisted of 15 502 individuals with laboratory-confirmed SARS-CoV-2. The main prediction model was developed using data from 11 635 individuals with 709 reported deaths (case fatality ratio 6.1%). The final prediction model consisted of 14 variables with 11 comorbidities. This model was then prospectively assessed among the remaining 3867 individuals (185 deaths; case fatality ratio 4.8%). When compared with using an age threshold of 65 years, the 14-variable model detected 6% more of the individuals who would die from COVID-19. However, below age 45 years and its risk equivalent, there was no benefit to using the prediction model over age alone.DiscussionUsing a prediction model, such as the one described here, may help identify individuals who would most benefit from COVID-19 inoculation, and thereby may produce more dramatic initial drops in deaths through targeted vaccination.

Do racial and ethnic disparities in following stay-at-home orders influence COVID-19 health outcomes? A mediation analysis approach

Racial/ethnic disparities are among the top-selective underlying determinants associated with the disproportional impact of the COVID-19 pandemic on human mobility and health outcomes. This study jointly examined county-level racial/ethnic differences in compliance with stay-at-home orders and COVID-19 health outcomes during 2020, leveraging two-year geo-tracking data of mobile devices across ~4.4 million point-of-interests (POIs) in the contiguous United States. Through a set of structural equation modeling, this study quantified how racial/ethnic differences in following stay-at-home orders could mediate COVID-19 health outcomes, controlling for state effects, socioeconomics, demographics, occupation, and partisanship. Results showed that counties with higher Asian populations decreased most in their travel, both in terms of reducing their overall POIs’ visiting and increasing their staying home percentage. Moreover, counties with higher White populations experienced the lowest infection rate, while counties with higher African American populations presented the highest case-fatality ratio. Additionally, control variables, particularly partisanship, median household income, percentage of elders, and urbanization, significantly accounted for the county differences in human mobility and COVID-19 health outcomes. Mediation analyses further revealed that human mobility only statistically influenced infection rate but not case-fatality ratio, and such mediation effects varied substantially among racial/ethnic compositions. Last, robustness check of racial gradient at census block group level documented consistent associations but greater magnitude. Taken together, these findings suggest that US residents’ responses to COVID-19 are subject to an entrenched and consequential racial/ethnic divide.

Systematic Assessment of COVID-19 Pandemic in Bangladesh: Effectiveness of Preparedness in the First Wave

Background: To develop an effective countermeasure and determine our susceptibilities to the outbreak of COVID-19 is challenging for a densely populated developing country like Bangladesh and a systematic review of the disease on a continuous basis is necessary.Methods: Publicly available and globally acclaimed datasets (4 March 2020–30 September 2020) from IEDCR, Bangladesh, JHU, and ECDC database are used for this study. Visual exploratory data analysis is used and we fitted a polynomial model for the number of deaths. A comparison of Bangladesh scenario over different time points as well as with global perspectives is made.Results: In Bangladesh, the number of active cases had decreased, after reaching a peak, with a constant pattern of death rate at from July to the end of September, 2020. Seventy-one percent of the cases and 77% of the deceased were males. People aged between 21 and 40 years were most vulnerable to the coronavirus and most of the fatalities (51.49%) were in the 60+ population. A strong positive correlation (0.93) between the number of tests and confirmed cases and a constant incidence rate (around 21%) from June 1 to August 31, 2020 was observed. The case fatality ratio was between 1 and 2. The number of cases and the number of deaths in Bangladesh were much lower compared to other countries.Conclusions: This study will help to understand the patterns of spread and transition in Bangladesh, possible measures, effectiveness of the preparedness, implementation gaps, and their consequences to gather vital information and prevent future pandemics.

Healthcare under pressure: modelling COVID-19 fatalities with multiscale geographically weighted regressions

PurposeThe paper aims to disentangle the factors behind territorial disparities in the coronavirus disease 2019 (COVID-19) case fatality ratio, focusing on the pressure put by the pandemic on healthcare services and adopting a spatial perspective.Design/methodology/approachMultiscale geographically weighted regression (MGWR) models have been used for uncovering the spatial variability in the impact of healthcare services on COVID-19 case fatality ratio, allowing authors to better capture the real spatial patterns at local level. The authors proved that this approach yields better results, and the MGWR model outperforms traditional regression methods. The selected case studies are two of the biggest UE countries, among the first affected by a high incidence of COVID-19 cases, namely Italy and Germany.FindingsThe authors found sizeable regional differences in COVID-19 mortality rates within each of the analysed countries, and the stress borne by local healthcare systems seems to be the most powerful factor in explaining them. In line with other studies, the authors found additional factors of influence, such as age distribution, gender ratio, population density and regional development.Originality/valueThis research clearly indicated that COVID-19 related deaths are strongly associated with the degree of resilience of the local healthcare systems. The authors supply localized results on the factors of influence, useful for assisting the decision-makers in prioritizing limited healthcare resources. The authors provide a scientific argument in favour of the decentralization of the pandemic management towards local authorities not neglecting, however, the necessary regional or national coordination.