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Since December 2019, the COVID-19 pandemic has had a major impact on global health and the economy. Epidemiological forecasts are crucial for governmental decisions, healthcare officials, and the general public. A collaboration between the Institute of Global Health at the University of Geneva and the Swiss Data Science Center created an interactive dashboard providing forecasts for over 200 countries and territories. This dashboard has been a valuable tool for the public and authorities alike. The pandemic has highlighted the importance of international collaborations and a robust national surveillance system. Data collection systems, pathogen-agnostic models, and communication tools need to be consolidated and maintained in operation., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Competing Interests: AF is Chair of and AC, DC, OD, IE, HJL, HL-Q, CN, and MK are members of the High-Level Expert Group for COVID-19 that has advised Hans Kluge since 2021. HK is the Regional Director of the WHO Regional Office for Europe. AC report grants from ViiV Healthcare, Gilead Sciences, and MSD and is the principal investigator of the OPTICOV trial. DC report institutional grant from Janssen and honoraria for lectures from Gilead and Pfizer. HJL reports grants from GSK, Merck, and a MacArthur Award for the Vaccine Confidence Project and honoraria for lectures from UNC and NYU. HL-Q was a member of the Secretariat of the Independent Panel for Pandemic Preparedness and Response. CN reports grants from the Engineering and Physical Sciences Research Council, the UK Department of Health and Social Care, the Natural Environment Research Council, NHS Scotland, and the UK Government for various projects on transmission of COVID-19; is a member of the UK Government SAGE Committee, Co-Chair of SAGE Environment and Modelling group, is a member of multiple UK and Scottish Government and NHS working groups on COVID-19, is a member of WHO ECAP, and a member of the Royal Academy of Engineering and Academy of Medical Sciences working groups on COVID-19 mitigation. MK is an independent Board member of ExevirBio, a biotechnology company designing therapeutic monoclonal antibodies against SARS Cov2. We declare no other competing interests. We thank Catherine Smallwood, Gerald Rockenschaub, and Richard Pebody, from the WHO Regional Office for Europe, for their participation in reviewing and their helpful contributions to this Comment.

No study has yet investigated if a severe SARS-CoV-2 infection represents a marker of an undiagnosed cancer. This population-based study, using the SNDS database, identified from 02/15/2020 to 08/31/2021, 41,302 individuals hospitalized in intensive care unit due to SARS-CoV-2 (ICU-gr) and 713,670 control individuals not hospitalized for SARS-CoV-2 (C-gr). Individuals were matched according to year of birth, sex and French department. The cancer incidence was compared in the two groups during the follow-up period (index date-12/31/2021), using Cox proportional hazards models adjusted on matching variables, socioeconomic characteristics and comorbidities. In the ICU-gr, 2.2% (n = 897) was diagnosed with a cancer in the following months, compared to 1.5% (n = 10,944) in the C-gr. The ICU-gr had a 1.31 higher risk of being diagnosed with a cancer following hospital discharge compared to the C-gr (aHR 1.31, 95% CI 1.22-1.41). A global similar trend was found when competing risk of death was taken into account (aHR 1.25, 95% CI 1.16-1.34). A significant higher risk was found concerning renal (aHR 3.16, 95% CI 2.33-4.27), hematological (aHR 2.54, 95% CI 2.07-3.12), colon (aHR 1.72, 95% CI 1.34-2.21), and lung (aHR 1.70, 95% CI 1.39-2.08) cancers. This suggests that a severe SARS-CoV-2 infection may represent a marker of an undiagnosed cancer., (© 2023. The Author(s).)

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

The "One Health" approach is essential to better understand and manage a pandemic of animal origin. Sensitive geopolitical considerations seem to hamper the investigations into the origin of the pandemic, but everything points to the Rhinolophus bat as the starting point of this devastating pandemic. Through a phenomenon of reverse zoonosis, several hundred cases of contamination of animals by SARS-CoV-2 have been identified worldwide, involving about twenty species of mammals. The virus has also passed from animals to humans in the case of infected mink farms in Denmark or through contact with hamsters in Hong Kong. For the development of vaccines and treatments and to help detect COVID-19 in train stations or airports, the animal has confirmed its role as a valuable auxiliary resource for humans in the fight against the pandemic., Competing Interests: Les auteur-e-s n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Aims: To assess SARS-CoV-2 seroprevalence over the first epidemic wave in the canton of Geneva, Switzerland, as well as risk factors for infection and symptoms associated with IgG seropositivity. Methods: Between April and June 2020, former participants of a representative survey of the 20-74-year-old population of canton Geneva were invited to participate in the study, along with household members aged over 5 years. Blood samples were tested for anti-SARS-CoV-2 immunoglobulin G. Questionnaires were self-administered. We estimated seroprevalence with a Bayesian model accounting for test performance and sampling design. Results: We included 8344 participants, with an overall adjusted seroprevalence of 7.8% (95% credible interval 6.8-8.9). Seroprevalence was highest among 18-49 year-olds (9.5%), and lowest in 5-9-year-old children (4.3%) and individuals >65 years (4.7-5.4%). Odds of seropositivity were significantly reduced for female retirees and unemployed men compared to employed individuals, and smokers compared to non-smokers. We found no significant association between occupation, level of education, neighborhood income and the risk of being seropositive. The symptom most strongly associated with seropositivity was anosmia/dysgeusia. Conclusions: Anti-SARS-CoV-2 population seroprevalence remained low after the first wave in Geneva. Socioeconomic factors were not associated with seropositivity in this sample. The elderly, young children and smokers were less frequently seropositive, although it is not clear how biology and behaviours shape these differences.

Unrealistic optimism, the underestimation of one's risk of experiencing harm, has been investigated extensively to understand better and predict behavioural responses to health threats. Prior to the COVID-19 pandemic, a relative dearth of research existed in this domain regarding epidemics, which is surprising considering that this optimistic bias has been associated with a lack of engagement in protective behaviours critical in fighting twenty-first-century, emergent, infectious diseases. The current study addresses this gap in the literature by investigating whether people demonstrated optimism bias during the first wave of the COVID-19 pandemic in Europe, how this changed over time, and whether unrealistic optimism was negatively associated with protective measures. Taking advantage of a pre-existing international participative influenza surveillance network ( n = 12,378), absolute and comparative unrealistic optimism were measured at three epidemic stages (pre-, early, peak), and across four countries-France, Italy, Switzerland and the United Kingdom. Despite differences in culture and health response, similar patterns were observed across all four countries. The prevalence of unrealistic optimism appears to be influenced by the particular epidemic context. Paradoxically, whereas absolute unrealistic optimism decreased over time, comparative unrealistic optimism increased, suggesting that whilst people became increasingly accurate in assessing their personal risk, they nonetheless overestimated that for others. Comparative unrealistic optimism was negatively associated with the adoption of protective behaviours, which is worrying, given that these preventive measures are critical in tackling the spread and health burden of COVID-19. It is hoped these findings will inspire further research into sociocognitive mechanisms involved in risk appraisal.

The World Health Organization African region recorded its first laboratory-confirmed coronavirus disease-2019 (COVID-19) cases on 25 February 2020. Two months later, all the 47 countries of the region were affected. The first anniversary of the pandemic occurred in a changed context with the emergence of new variants of concern (VOC) and growing COVID-19 fatigue. This study describes the epidemiological trajectory of COVID-19 in the region, summarises public health and social measures (PHSM) implemented and discusses their impact on the pandemic trajectory. As of 24 February 2021, the African region accounted for 2.5% of cases and 2.9% of deaths reported globally. Of the 13 countries that submitted detailed line listing of cases, the proportion of cases with at least one co-morbid condition was estimated at 3.3% of all cases. Hypertension, diabetes and human immunodeficiency virus (HIV) infection were the most common comorbid conditions, accounting for 11.1%, 7.1% and 5.0% of cases with comorbidities, respectively. Overall, the case fatality ratio (CFR) in patients with comorbid conditions was higher than in patients without comorbid conditions: 5.5% vs. 1.0% (P < 0.0001). Countries started to implement lockdown measures in early March 2020. This contributed to slow the spread of the pandemic at the early stage while the gradual ease of lockdowns from 20 April 2020 resulted in an upsurge. The second wave of the pandemic, which started in November 2020, coincided with the emergence of the new variants of concern. Only 0.08% of the population from six countries received at least one dose of the COVID-19 vaccine. It is critical to not only learn from the past 12 months to improve the effectiveness of the current response but also to start preparing the health systems for subsequent waves of the current pandemic and future pandemics.

As of 03 January 2021, the WHO African region is the least affected by the coronavirus disease-2019 (COVID-19) pandemic, accounting for only 2.4% of cases and deaths reported globally. However, concerns abound about whether the number of cases and deaths reported from the region reflect the true burden of the disease and how the monitoring of the pandemic trajectory can inform response measures.We retrospectively estimated four key epidemiological parameters (the total number of cases, the number of missed cases, the detection rate and the cumulative incidence) using the COVID-19 prevalence calculator tool developed by Resolve to Save Lives. We used cumulative cases and deaths reported during the period 25 February to 31 December 2020 for each WHO Member State in the region as well as population data to estimate the four parameters of interest. The estimated number of confirmed cases in 42 countries out of 47 of the WHO African region included in this study was 13 947 631 [95% confidence interval (CI): 13 334 620-14 635 502] against 1 889 512 cases reported, representing 13.5% of overall detection rate (range: 4.2% in Chad, 43.9% in Guinea). The cumulative incidence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was estimated at 1.38% (95% CI: 1.31%-1.44%), with South Africa the highest [14.5% (95% CI: 13.9%-15.2%)] and Mauritius [0.1% (95% CI: 0.099%-0.11%)] the lowest. The low detection rate found in most countries of the WHO African region suggests the need to strengthen SARS-CoV-2 testing capacities and adjusting testing strategies.

The COVID-19 outbreak is a global health concern. Understanding the transmission modes of the SARS-CoV-2 virus is key to limit the spread of the pandemic. A lack of knowledge about the possibility of SARS-CoV-2 transmission and infection through contaminated surfaces is noticeable and recent studies have stated conflicting findings. This scoping review aims to understand the risks of contaminations via fomites better. Relevant publications were selected through Google Scholar, Web of Science, PubMed, Embase, Medline, and Cochrane Library, with related keywords. PRISMA-ScR guidelines were followed. Out of the 565 articles found, exclusion criteria were applied, duplicates removed, and a total of 25 articles were finally included in the study. The included documents were assessed by the contamination risk: "low" (37.5%), "high" (16.7%), "plausible" (8.3%), "unlikely" (8.3%) risk, and "insufficient evidence" (29.2%). Research in hospital settings was found as the main setting in the reviewed papers, which precisely indicated the risk of contaminated surfaces. This scoping review underscores the risk of SARS-CoV-2 infection via contaminated surfaces assessed as low in the majority of the reviewed articles. Further evaluation of the risk of the virus transmission by fomites and providing adequate information on its infectivity via contaminated surfaces in real-life conditions is essential.

Experience gained from responding to major outbreaks may have influenced the early coronavirus disease-2019 (COVID-19) pandemic response in several countries across Africa. We retrospectively assessed whether Guinea, Liberia and Sierra Leone, the three West African countries at the epicentre of the 2014-2016 Ebola virus disease outbreak, leveraged the lessons learned in responding to COVID-19 following the World Health Organization's (WHO) declaration of a public health emergency of international concern (PHEIC). We found relatively lower incidence rates across the three countries compared to many parts of the globe. Time to case reporting and laboratory confirmation also varied, with Guinea and Liberia reporting significant delays compared to Sierra Leone. Most of the selected readiness measures were instituted before confirmation of the first case and response measures were initiated rapidly after the outbreak confirmation. We conclude that the rapid readiness and response measures instituted by the three countries can be attributed to their lessons learned from the devastating Ebola outbreak, although persistent health systems weaknesses and the unique nature of COVID-19 continue to challenge control efforts.

Background: The WHO's Research and Development Blueprint priority list designates emerging diseases with the potential to generate public health emergencies for which insufficient preventive solutions exist. The list aims to reduce the time to the availability of resources that can avert public health crises. The current SARS-CoV-2 pandemic illustrates that an effective method of mitigating such crises is the pre-emptive prediction of outbreaks. This scoping review thus aimed to map and identify the evidence available to predict future outbreaks of the Blueprint diseases., Methods: We conducted a scoping review of PubMed, Embase and Web of Science related to the evidence predicting future outbreaks of Ebola and Marburg virus, Zika virus, Lassa fever, Nipah and Henipaviral disease, Rift Valley fever, Crimean-Congo haemorrhagic fever, Severe acute respiratory syndrome, Middle East respiratory syndrome and Disease X. Prediction methods, outbreak features predicted and implementation of predictions were evaluated. We conducted a narrative and quantitative evidence synthesis to highlight prediction methods that could be further investigated for the prevention of Blueprint diseases and COVID-19 outbreaks., Results: Out of 3959 articles identified, we included 58 articles based on inclusion criteria. 5 major prediction methods emerged; the most frequent being spatio-temporal risk maps predicting outbreak risk periods and locations through vector and climate data. Stochastic models were predominant. Rift Valley fever was the most predicted disease. Diseases with complex sociocultural factors such as Ebola were often predicted through multifactorial risk-based estimations. 10% of models were implemented by health authorities. No article predicted Disease X outbreaks., Conclusions: Spatiotemporal models for diseases with strong climatic and vectorial components, as in River Valley fever prediction, may currently best reduce the time to the availability of resources. A wide literature gap exists in the prediction of zoonoses with complex sociocultural and ecological dynamics such as Ebola, COVID-19 and especially Disease X., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The current global systemic crisis reveals how globalised societies are unprepared to face a pandemic. Beyond the dramatic loss of human life, the COVID-19 pandemic has triggered widespread disturbances in health, social, economic, environmental and governance systems in many countries across the world. Resilience describes the capacities of natural and human systems to prevent, react to and recover from shocks. Societal resilience to the current COVID-19 pandemic relates to the ability of societies in maintaining their core functions while minimising the impact of the pandemic and other societal effects. Drawing on the emerging evidence about resilience in health, social, economic, environmental and governance systems, this paper delineates a multisystemic understanding of societal resilience to COVID-19. Such an understanding provides the foundation for an integrated approach to build societal resilience to current and future pandemics., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Understanding the risk of infection from household- and community-exposures and the transmissibility of asymptomatic infections is critical to SARS-CoV-2 control. Limited previous evidence is based primarily on virologic testing, which disproportionately misses mild and asymptomatic infections. Serologic measures are more likely to capture all previously infected individuals. We apply household transmission models to data from a cross-sectional, household-based population serosurvey of 4,534 people ≥5 years from 2,267 households enrolled April-June 2020 in Geneva, Switzerland. We found that the risk of infection from exposure to a single infected household member aged ≥5 years (17.3%,13.7-21.7) was more than three-times that of extra-household exposures over the first pandemic wave (5.1%,4.5-5.8). Young children had a lower risk of infection from household members. Working-age adults had the highest extra-household infection risk. Seropositive asymptomatic household members had 69.4% lower odds (95%CrI,31.8-88.8%) of infecting another household member compared to those reporting symptoms, accounting for 14.5% (95%CrI, 7.2-22.7%) of all household infections.

Successive waves of COVID-19 transmission have led to exponential increases in new infections globally. In this study, we have applied a decision-making tool to assess the risk of continuing transmission to inform decisions on tailored public health and social measures (PHSM) using data on cases and deaths reported by Member States to the WHO Regional Office for Africa as of 31 December 2020. Transmission classification and health system capacity were used to assess the risk level of each country to guide implementation and adjustments to PHSM. Two countries out of 46 assessed met the criteria for sporadic transmission, one for clusters of cases, and 43 (93.5%) for community transmission (CT) including three with uncontrolled disease incidence (Eswatini, Namibia and South Africa). Health system response's capacities were assessed as adequate in two countries (4.3%), moderate in 13 countries (28.3%) and limited in 31 countries (64.4%). The risk level, calculated as a combination of transmission classification and health system response's capacities, was assessed at level 0 in one country (2.1%), level 1 in two countries (4.3%), level 2 in 11 countries (23.9%) and level 3 in 32 (69.6%) countries. The scale of severity ranged from 0 to 4, with 0 the lowest. CT coupled with limited response capacity resulted in a level 3 risk assessment in most countries. Countries at level 3 should be considered as priority focus for additional assistance, in order to prevent the risk rising to level 4, which may necessitate enforcing hard and costly lockdown measures. The large number of countries at level 3 indicates the need for an effective risk management system to be used as a basis for adjusting PHSM at national and sub-national levels.

Monitoring and evaluation (M&E) is an essential component of public health emergency response. In the WHO African region (WHO AFRO), over 100 events are detected and responded to annually. Here we discuss the development of the M&E for COVID-19 that established a set of regional and country indicators for tracking the COVID-19 pandemic and response measures. An interdisciplinary task force used the 11 pillars of strategic preparedness and response to define a set of inputs, outputs, outcomes and impact indicators that were used to closely monitor and evaluate progress in the evolving COVID-19 response, with each pillar tailored to specific country needs. M&E data were submitted electronically and informed country profiles, detailed epidemiological reports, and situation reports. Further, 10 selected key performance indicators were tracked to monitor country progress through a bi-weekly progress scoring tool used to identify priority countries in need of additional support from WHO AFRO. Investment in M&E of health emergencies should be an integral part of efforts to strengthen national, regional and global capacities for early detection and response to threats to public health security. The development of an adaptable M&E framework for health emergencies must draw from the lessons learned throughout the COVID-19 response.

It is crucial to understand how the most vulnerable populations have been impacted by the ongoing COVID-19 pandemic. This paper intends to contextualize the experience of resettled refugees in Canada during the COVID-19 pandemic, framing the issue for further study as the situation evolves. Based on the experience drawn from the first wave of the pandemic, the findings of this paper suggest that refugees in Canada encounter barriers to healthcare, economic support, education, social support, and border crossing impediments, all of which can have a compounding effect. These findings provide needed information to inform the development of effective policies and strategies to support refugees during health security emergencies in Canada.

The outbreak of the novel coronavirus SARS-CoV-2 and the disease it causes, COVID-19, which emerged in 2019, was identified by the World Health Organization as a public health emergency of international concern. Brazil actively responded to contain the virus. This case study aims to examine Brazil's response to COVID-19 by investigating the country's actions and reflecting upon the outcomes throughout January and March 2020. The data collection strategy included gathering data from the country's intergovernmental organization's official website, epidemiological bulletins, and news reports, guided by intersectoral and interdisciplinary themes. Although the highest incidence rates were in the most rich and populated region in Brazil, it was the poorest region that had the highest case fatality rate. Nevertheless, Brazil took several non-pharmaceutical measures to control and mitigate the spread of the virus. However, the strategy seems to have failed to consider regional and social inequalities. The actions of the health minister were undermined by a conflicting discourse between the minister and the president. The outbreak of COVID-19 added an extra burden on the country's healthcare system and the existing economic crises; exacerbated the inherent social, political, and economic challenges; and exposed the country's contradictions.

The coronavirus disease 2019 (COVID-19) pandemic has affected millions of people worldwide. It brought about the implementation of various measures and restrictions at a global level. Iran has been one of the countries with the highest rates of COVID-19 cases. This study reviews the initial outbreak of COVID-19 in Iran and examines the mitigation strategies adopted by the country. Moreover, it reports the socioeconomic challenges faced by the authorities during the efforts to contain the virus. A transdisciplinary literature review was carried out and a political measures timeline was constructed. A broad overview of the initial phase of the COVID-19 outbreak in Iran is presented, starting from the first confirmed case on 19 February 2020 until April 2020. The results of this epidemiological and socioeconomic case review of Iran suggests that the political measures undertaken by the Republic of Iran contributed to the decrease of the prevalence of COVID-19. However, due to the existing financial bottleneck, Iran has faced limited health system resources. Therefore, the response was not sufficient to restrict the spread and the efficient handling of the virus in the long-term.

This article synthesizes the results of case studies on the development of the coronavirus disease 2019 (COVID-19) pandemic and control measures by governments in 16 countries. When this work was conducted, only 6 months had passed since the pandemic began, and only 4 months since the first events were recognized outside of China. It was too early to draw firm conclusions about the effectiveness of measures in each of the selected countries; however, the authors present some efforts to identify and classify response and containment measures, country-by-country, for future comparison and analysis. There is a significant variety of policy tools and response measures employed in different countries, and while it is still hard to directly compare the different approaches based on their efficacy, it will definitely provide many inputs for the future data analysis efforts.

In this article, we investigate the impact of COVID-19 through screening and surveillance methods adopted in India, as well as the potential health system, social, political, and economic consequences. The research was done in a chronological manner, and data was collected between 30 January 2020 till 12 June 2020. Initial containment measures, including point of entry screenings and testing protocols, appeared insufficient. However, testing capacity was gradually expanded after the commencement of a nation-wide lockdown. Modeling predictions have shown varying results on the emergence of cases depending on the infectiousness of asymptomatic individuals, with a peak predicted in mid-July having over two million cases. The country also faces risks of the economic plunge by losing approximately 4% of its gross domestic product, due to containment measures and reduction in goods importation. The low public health expenditure combined with a lack of infrastructure and low fiscal response implies several challenges to scale up the COVID-19 response and management. Therefore, an emergency preparedness and response plan is essential to integrate into the health system of India.

The following case study aims to provide a broad overview of the initial Australian epidemiological situation of the novel coronavirus disease (COVID-19) pandemic. We provide a case presentation of Australia's current demographic characteristics and an overview of their health care system. The data we present on Australia's COVID-19 situation pertain to the initial wave of the pandemic from January through to 20 April 2020. The results of our study indicate the number of reported COVID-19 cases in Australia reduced, and Australia initially managed to successfully flatten the curve-from an initial doubling time of 3.4 days at the end of March 2020 to a doubling time of 112 days as of 20 April 2020. Using SEIR mathematical modelling, we investigate a scenario assuming infections increase once mitigation measures are lifted. In this case, Australia could experience over 15,000 confirmed cases by the end of April 2020. How Australia's government, health authorities and citizens adjust to preventative measures to reduce the risk of transmission as well as the risk of overburdening Australia's health care system is crucial. Our study presents the initial non-pharmaceutical intervention measures undertaken by the Australian health authorities in efforts to mitigate the rate of infection, and their observed and predicted outcomes. Finally, we conclude our study by presenting the observed and expected economic, social, and political disruptions Australians may endure as a result of the initial phase of the pandemic.

Objectives: Seroprevalence studies to assess the spread of SARS-CoV-2 infection in the general population and subgroups are key for evaluating mitigation and vaccination policies and for understanding the spread of the disease both on the national level and for comparison with the international community., Methods: Corona Immunitas is a research program of coordinated, population-based, seroprevalence studies implemented by Swiss School of Public Health (SSPH+). Over 28,340 participants, randomly selected and age-stratified, with some regional specificities will be included. Additional studies in vulnerable and highly exposed subpopulations are also planned. The studies will assess population immunological status during the pandemic., Results: Phase one (first wave of pandemic) estimates from Geneva showed a steady increase in seroprevalence up to 10.8% (95% CI 8.2-13.9, n = 775) by May 9, 2020. Since June, Zurich, Lausanne, Basel City/Land, Ticino, and Fribourg recruited a total of 5973 participants for phase two thus far., Conclusions: Corona Immunitas will generate reliable, comparable, and high-quality serological and epidemiological data with extensive coverage of Switzerland and of several subpopulations, informing health policies and decision making in both economic and societal sectors. ISRCTN Registry: https://www.isrctn.com/ISRCTN18181860 .

The recent lifting of COVID-19 related restrictions in Switzerland causes uncertainty about the future of the epidemic. We developed a compartmental model for SARS-CoV-2 transmission in Switzerland and projected the course of the epidemic until the end of year 2020 under various scenarios. The model was age-structured with three categories: children (0-17), adults (18-64) and seniors (65- years). Lifting all restrictions according to the plans disclosed by the Swiss federal authorities by mid-May resulted in a rapid rebound in the epidemic, with the peak expected in July. Measures equivalent to at least 90% reduction in all contacts were able to eradicate the epidemic; a 56% reduction in contacts could keep the intensive care unit occupancy under the critical level and delay the next wave until October. In scenarios where strong contact reductions were only applied in selected age groups, the epidemic could not be suppressed, resulting in an increased risk of a rebound in July, and another stronger wave in September. Future interventions need to cover all age groups to keep the SARS-CoV-2 epidemic under control., Competing Interests: No competing interests were disclosed., (Copyright: © 2020 Estill J et al.)

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness. [ABSTRACT FROM AUTHOR]

Objectives: The COVID-19 pandemic has been a major public health concern for the past 3 years. Scientific evidence on the relationship between SARS-CoV-2 infection and indoor air quality still needs to be demonstrated. This scoping review aims to study the association between air quality indoors and COVID-19. Methods: A scoping review analyzing the association between indoor air quality and epidemiological outcomes was conducted. Papers published between 1 January 2020 and 31 October 2022 were included. Hospital settings were excluded from the study. Results: Eight relevant articles met the inclusion criteria. Indoor settings included workplaces, schools, restaurants, and public transport. Types of ventilation used to improve indoor air quality were dilution methods (opening windows) and mechanical systems with or without filtration or purifier. CO2 sensors were employed in one study. All the studies showed a positive association between indoor air quality and its improvement and epidemiological indicators. Conclusion: The findings of this scoping review indicate that indoor air quality, which can be improved with ventilation methods, may reduce the risk of developing COVID-19. Ventilation could thus be viewed as a possible effective mitigating method. [ABSTRACT FROM AUTHOR]

This study describes risk factors associated with mortality among COVID-19 cases reported in the WHO African region between 21 March and 31 October 2020. Average hazard ratios of death were calculated using weighted Cox regression as well as median time to death for key risk factors. We included 46 870 confirmed cases reported by eight Member States in the region. The overall incidence was 20.06 per 100 000, with a total of 803 deaths and a total observation time of 3 959 874 person-days. Male sex (aHR 1.54 (95% CI 1.31–1.81); P < 0.001), older age (aHR 1.08 (95% CI 1.07–1.08); P < 0.001), persons who lived in a capital city (aHR 1.42 (95% CI 1.22–1.65); P < 0.001) and those with one or more comorbidity (aHR 36.37 (95% CI 20.26–65.27); P < 0.001) had a higher hazard of death. Being a healthcare worker reduced the average hazard of death by 40% (aHR 0.59 (95% CI 0.37–0.93); P = 0.024). Time to death was significantly less for persons ≥60 years (P = 0.038) and persons residing in capital cities (P < 0.001). The African region has COVID-19-related mortality similar to that of other regions, and is likely underestimated. Similar risk factors contribute to COVID-19-associated mortality as identified in other regions. [ABSTRACT FROM AUTHOR]

The first wave of coronavirus disease 2019 (COVID-19) pandemic is currently invading the world, and several countries are now struggling to fight it or trying to delay its start to help smooth its peak size for the purpose of lowering morbidity and mortality, and thereby reduce the overall tension on their health-care system. China's first major outbreaks of COVID-19 happened in January, 2020. [Extracted from the article]