•Our phylogenetic analysis reveals the Iranian epidemic started nearly two months before when the first cases were reported.•By analysing 46 genomic samples of SARS-CoV-2 from Iran we find at least 5 ...independent introductions into the country in 2020.•Analyses of air travel data and registered deaths revel significant levels of under-reporting during the first and second wave.•The epidemic was likely seeded by only a few imported cases from China that belong to a high-risk group with frequent travels.•We reconstruct early transmission dynamics in Iran and predicted the relaxation of NPIs would have led to a second peak.
Many countries with an early outbreak of SARS-CoV-2 struggled to gauge the size and start date of the epidemic mainly due to limited testing capacities and a large proportion of undetected asymptomatic and mild infections. Iran was among the first countries with a major outbreak outside China.
We constructed a globally representative sample of 802 genomes, including 46 samples from patients inside or with a travel history to Iran. We then performed a phylogenetic analysis to identify clades related to samples from Iran and estimated the start of the epidemic and early doubling times in cases. We leveraged air travel data from 36 exported cases of COVID-19 to estimate the point-prevalence and the basic reproductive number across the country. We also analysed the province-level all-cause mortality data during winter and spring 2020 to estimate under-reporting of COVID-19-related deaths. Finally, we use this information in an SEIR model to reconstruct the early outbreak dynamics and assess the effectiveness of intervention measures in Iran.
By identifying the most basal clade that contained genomes from Iran, our phylogenetic analysis showed that the age of the root is placed on 2019-12-21 (95 % HPD: 2019-09-07 – 2020-02-14). This date coincides with our estimated epidemic start date on 2019-12-25 (95 %CI: 2019-12-11 – 2020-02-24) based air travel data from exported cases with an early doubling time of 4.0 (95 %CI: 1.4–6.7) days in cases. Our analysis of all-cause mortality showed 21.9 (95 % CI: 16.7–27.2) thousand excess deaths by the end of summer. Our model forecasted the second epidemic peak and suggested that by 2020-08-31 a total of 15.0 (95 %CI: 4.9–25.0) million individuals recovered from the disease across the country.
These findings have profound implications for assessing the stage of the epidemic in Iran despite significant levels of under-reporting. Moreover, the results shed light on the dynamics of SARS-CoV-2 transmissions in Iran and central Asia. They also suggest that in the absence of border screening, there is a high risk of introduction from travellers from areas with active outbreaks. Finally, they show both that well-informed epidemic models are able to forecast episodes of resurgence following a relaxation of interventions, and that NPIs are key to controlling ongoing epidemics.
•Impact of risk perceptions associated with COVID-19 on travel mode switch.•Negative effect of the perceived susceptibility on intentions to choose the collective over the individual travel ...mode.•Risk-taking behavior exhibits the highest effect.•Contact with risk group or having positive corona test shows no effect.
The coronavirus pandemic (COVID-19) led to holiday journeys being associated with significant health risks. While there are numerous studies on the impacts of the COVID-19 pandemic on travel mode choice in everyday mobility, there are a lack of studies on tourists’ choice of travel mode, even though tourism transport in Switzerland makes up 24% of all distance travelled. Based on an extended conceptual framework of the Health Belief Model (HBM), this study investigates the effect of COVID-19 on tourists’ intentions to choose a particular travel mode during the pandemic. Our findings show that the higher the perceived susceptibility of getting COVID-19 associated with the holiday journey, the lower the choices for collective travel modes. Furthermore, for those tourists who are more likely to take risks, their choices for collective travel modes are increased. The study recommends that public transport operators choose measures that increase the application of non-pharmaceutical interventions (NPIs) against pandemics while travelling; this may encourage the safe use of collective transport modes during a pandemic.
The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological ...interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19, with the model parameters enabling an evaluation of the impact of NPIs. By representing the number of daily confirmed cases (NDCC) as a time-series, we assume that, with or without NPIs, the pattern of the pandemic satisfies a series of Gaussian distributions according to the central limit theorem. The underlying pandemic trend is first extracted using a singular spectral analysis (SSA) technique, which decomposes the NDCC time series into the sum of a small number of independent and interpretable components such as a slow varying trend, oscillatory components and structureless noise. We then use a mixture of Gaussian fitting (GF) to derive a novel predictive model for the SSA extracted NDCC incidence trend, with the overall model termed SSA-GF. Our proposed model is shown to accurately predict the NDCC trend, peak daily cases, the length of the pandemic period, the total confirmed cases and the associated dates of the turning points on the cumulated NDCC curve. Further, the three key model parameters, specifically, the amplitude (alpha), mean (mu), and standard deviation (sigma) are linked to the underlying pandemic patterns, and enable a directly interpretable evaluation of the impact of NPIs, such as strict lockdowns and travel restrictions. The predictive model is validated using available data from China and South Korea, and new predictions are made, partially requiring future validation, for the cases of Italy, Spain, the UK and the USA. Comparative results demonstrate that the introduction of consistent control measures across countries can lead to development of similar parametric models, reflected in particular by relative variations in their underlying sigma, alpha and mu values. The paper concludes with a number of open questions and outlines future research directions.
China implemented strict non-pharmaceutical interventions to contain COVID-19 at the early stage. We aimed to evaluate the impact of COVID-19 on HIV care continuum in China.
Aggregated data on HIV ...care continuum between 1 January 2017 and 31 December 2020 were collected from centers for disease control and prevention at different levels and major infectious disease hospitals in various regions in China. We used interrupted time series analysis to characterize temporal trend in weekly numbers of HIV post-exposure prophylaxis (PEP) prescriptions, HIV tests, HIV diagnoses, median time intervals between HIV diagnosis and antiretroviral therapy (ART) initiation (time intervals, days), ART initiations, mean CD4+ T cell counts at ART initiation (CD4 counts, cells/μL), ART collections, and missed visits for ART collection, before and after the implementation of massive NPIs (23 January to 7 April 2020). We used Poisson segmented regression models to estimate the immediate and long-term impact of NPIs on these outcomes.
A total of 16,780 PEP prescriptions, 1,101,686 HIV tests, 69,659 HIV diagnoses, 63,409 time intervals and ART initiations, 61,518 CD4 counts, 1,528,802 ART collections, and 6656 missed visits were recorded during the study period. The majority of outcomes occurred in males (55·3-87·4%), 21-50 year olds (51·7-90·5%), Southwestern China (38·2-82·0%) and heterosexual transmission (47·9-66·1%). NPIs was associated with 71·5% decrease in PEP prescriptions (IRR 0·285; 95% CI 0·192-0·423), 36·1% decrease in HIV tests (0·639, 0·497-0·822), 32·0% decrease in HIV diagnoses (0·680, 0·511-0·904), 59·3% increase in time intervals (1·593, 1·270-1·997) and 17·4% decrease in CD4 counts (0·826, 0·746-0·915) in the first week during NPIs. There was no marked change in the number of ART initiations, ART collections and missed visits during the NPIs. By the end of 2020, the number of HIV tests, HIV diagnoses, time intervals, ART initiations, and CD4 counts reached expected levels, but the number of PEP prescriptions (0·523, 0·394-0·696), ART collections (0·720, 0·595-0·872), and missed visits (0·137, 0·086-0·220) were still below expected levels. With the ease of restrictions, PEP prescriptions (slope change 1·024/week, 1·012-1·037), HIV tests (1·016/week, 1·008-1·026), and CD4 counts (1·005/week, 1·001-1·009) showed a significant increasing trend.
HIV care continuum in China was affected by the COVID-19 NPIs at various levels. Preparedness and efforts to maintain the HIV care continuum during public health emergencies should leverage collaborations between stakeholders.
Natural Science Foundation of China.
The year 2020 has been marked by the novel coronavirus, named Severe Acute Respiratory Syndrome 2 (SARS-CoV-2), which causes coronavirus disease COVID-19. The World Health Organization (WHO) declared ...a global pandemic on the 11
of March 2020 due to the spread of this very contagious virus throughout the world. Since the outbreak, we have gained many insights about the virus, its presence and persistence in the environment and its possible and most common transmission routes. Such knowledge about the virus is invaluable for establishing effective preventive and control measures (also referred to as Non-Pharmaceutical Interventions (NPIs)) that have become a key to tackling this pandemic in the absence of a SARS-CoV-2 vaccine. In this review, we discuss five main groups of NPIs: 1) ventilation, 2) cleaning and disinfection, 3) hand hygiene, 4) physical distancing, and 5) protective masks. We explore their shortcomings and potential negative consequences that might occur as unwanted side effects.
Abstract Measures of seasonal influenza control are generally divided into two categories: pharmaceutical and non-pharmaceutical interventions. The effectiveness of these measures remains unclear, ...because of insufficient study sample size and/or differences in study settings. This observational epidemiological study involved all elementary schoolchildren in Matsumoto City, Japan, with seasonal influenza during the 2014/2015 season. Questionnaires, including experiences with influenza diagnosis and socio-demographic factors, were distributed to all 29 public elementary schools, involving 13,217 children, at the end of February 2015. Data were obtained from 10,524 children and analyzed with multivariate logistic regression analysis. The result showed that vaccination (odds ratio 0.866, 95% confidence interval 0.786–0.954) and wearing masks (0.859, 0.778–0.949) had significant protective association. Hand washing (1.447, 1.274–1.644) and gargling (1.319, 1.183–1.471), however, were not associated with protection. In the natural setting, hand washing and gargling showed a negative association, which may have been due to inappropriate infection control measures or aggregating infected and non-infected children to conduct those measures. These results may indicate a pathway for influenza transmission and explain why seasonal influenza control remains difficult in school settings. The overall effectiveness of vaccination and mask wearing was 9.9% and 8.6%, respectively. After dividing children into higher (grades 4–6) and lower (grade 1–3) grade groups, the effectiveness of vaccination became greater in the lower grade group, and the effectiveness of wearing masks became greater in the higher grade group. These results may provide valuable information about designing infection control measures that allocate resources among children.
A mathematical model is designed and used to study the transmission dynamics and control of COVID-19 in Nigeria. The model, which was rigorously analysed and parametrized using COVID-19 data ...published by the Nigeria Centre for Disease Control (NCDC), was used to assess the community-wide impact of various control and mitigation strategies in some jurisdictions within Nigeria (notably the states of Kano and Lagos, and the Federal Capital Territory, Abuja). Numerical simulations of the model showed that COVID-19 can be effectively controlled in Nigeria using moderate levels of social-distancing strategy in the jurisdictions and in the entire nation. Although the use of face masks in public can significantly reduce COVID-19 in Nigeria, its use, as a sole intervention strategy, may fail to lead to a substantial reduction in disease burden. Such substantial reduction is feasible in the jurisdictions (and the entire Nigerian nation) if the public face mask use strategy is complemented with a social-distancing strategy. The community lockdown measures implemented in Nigeria on March 30, 2020 need to be maintained for at least three to four months to lead to the effective containment of COVID-19 outbreaks in the country. Relaxing, or fully lifting, the lockdown measures sooner, in an effort to re-open the economy or the country, may trigger a deadly second wave of the pandemic.
•We examined the relative effectiveness of vaccination and NPIs on health outcomes and unemployment rate in the US.•The interaction between vaccinations and NPIs had a negative association with ...COVID-19 cases and deaths, implying some restrictions might be required even during vaccination campaigns and new variant surges.•Vaccinations had a negative association with the unemployment rate in the US, but the Omicron variant surge had a positive association with the total unemployment rate.•Policymakers should determine the optimal mix of NPIs and vaccinations to balance health and economic impacts, considering the potential psychological problems at the individual level.
Little is known about the relative effectiveness of COVID-19 vaccination and its interaction with non-pharmaceutical interventions (NPIs) in reducing infections, deaths, COVID-19 reproduction rate, and job losses. This study examined the relative effectiveness of vaccination and NPIs on COVID-19 infection, deaths, reproduction rate, and unemployment rate in the US.
Retrospective US data at the national level were obtained from the Oxford COVID-19 Government Response Tracker (OxCGRT dataset). Unemployment rate data were obtained from the US Bureau of Labor Statistics. Time-trend analyses of the policy variables and epidemiological outcomes were performed. A regression discontinuity in time was used to investigate the effects of policy variables on health outcomes and unemployment rate.
Based on time-trend analyses, the number of people vaccinated increased starting in March 2021, while the stringency index had steadily declined since early January 2021. A decrease in new COVID-19 cases and deaths was observed during this period. However, despite higher vaccination coverage, new COVID-19 cases and deaths peaked in late 2021 and early 2022. We found that the interaction between treatment effects (vaccinations) and stringency measures was negatively associated with total COVID-19 cases and deaths, implying that some restrictions might be required to reduce rising infections during vaccination campaigns. We also found a negative association between vaccinations and the unemployment rate.
The study findings suggested that vaccinations alone were insufficient to reduce virus spread and deaths, and that some NPIs might be required during the vaccination campaigns.
Abstract
Background
The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To ...prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020.
Methods
Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans.
Results
The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease.
Conclusions
The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.
School lockdowns have been widely used to control the COVID-19 pandemic. However, these lockdowns may have a significant negative impact on the lives of young people. In this study, we have evaluated ...the impact of closing lower secondary schools for COVID-19 incidence in 13–15-year-olds in Finland, in a situation where restrictions and recommendation of social distancing were implemented uniformly in the entire country. COVID-19 case numbers were obtained from the National Infectious Disease Registry (NIDR) of the Finnish Institute for Health and Welfare, in which clinical microbiology laboratories report all positive SARS-CoV-2 tests with unique identifiers in a timely manner. The NIDR is linked to population data registry, enabling calculation of incidences. We estimated the differences in trends between areas with both restaurant and lower secondary school closures and areas with only restaurant closures in different age groups by using joinpoint regression. We also estimated the differences in trends between age groups. Based on our analysis, closing lower secondary schools had no impact on COVID-19 incidence among 13–15-year-olds. No significant changes on COVID-19 incidence were observed in other age groups either.
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