COVID-19 epidemic phases and morbidity in different areas of Chinese mainland, 2020.

Background: In the early stage of COVID-19 epidemic, the Chinese mainland once effectively controlled the epidemic, but COVID-19 eventually spread faster and faster in the world. The purpose of this study is to clarify the differences in the epidemic data of COVID-19 in different areas and phases in Chinese mainland in 2020, and to analyze the possible factors affecting the occurrence and development of the epidemic. Methods: We divided the Chinese mainland into areas I, I and III, and divided the epidemic process into phases I to IV: limited cases, accelerated increase, decelerated increase and containment phases. We also combined phases II and III as outbreak phase. The epidemic data included the duration of different phases, the numbers of confirmed cases, asymptomatic infections, and the proportion of imported cases from abroad. Results: In area I, II and III, only area I has a Phase I, and the Phase II and III of area I are longer. In Phase IV, there is a 17-day case clearing period in area I, while that in area II and III are 2 and 0 days, respectively. In phase III or the whole outbreak phase, the average daily increase of confirmed cases in area I was higher than that in areas II and III (P = 0.009 and P = 0.001 in phase III; P = 0.034 and P = 0.002 in the whole outbreak phase), and the average daily in-hospital cases were most in area I and least in area III (P = 0.000, P = 0.000, and P = 0.000 in phase III; P = 0.000, P = 0.000, and P = 0.009 in the whole outbreak phase). The average number of daily in-hospital COVID-19 cases in phase III was more than that in phase II in each area (P = 0.000, P = 0.000, and P = 0.001). In phase IV, from March 18, 2020 to January 1, 2021, the increase of confirmed cases in area III was higher than areas I and II (both P = 0.000), and the imported cases from abroad in Chinese mainland accounted for more than 55-61%. From June 16 to July 2, 2020, the number of new asymptomatic infections in area III was higher than that in area II (P = 0.000), while there was zero in area I. From July 3, 2020 to January 1, 2021, the increased COVID-19 cases in area III were 3534, while only 14 and 0, respectively, in areas I and II. Conclusions: The worst epidemic areas in Chinese mainland before March 18, 2020 and after June 15, 2020 were area I and area III, respectively, and area III had become the main battlefield for Chinese mainland to fight against imported epidemic since March 18, 2020. In Wuhan, human COVID-19 infection might occur before December 8, 2019, while the outbreak might occur before January 16 or even 10, 2020. Insufficient understanding of COVID-19 hindered the implementation of early effective isolation measures, leading to COVID-19 outbreak in Wuhan, and strict isolation measures were effective in controlling the epidemic. The import of foreign COVID-19 cases has made it difficult to control the epidemic of area III. When humans are once again faced with potentially infectious new diseases, it is appropriate to first and foremost take strict quarantine measures as soon as possible, and mutual cooperation between regions should be explored to combat the epidemic.

COVID-19 epidemic and public health interventions in Shanghai, China: Statistical analysis of transmission, correlation and conversion.

Background: The Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown. Methods: We collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (C r ). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (R t ). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses. Results: (1) The overall level of R t in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, R t decreased rapidly, and the overall R t level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage. Conclusion: Effective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Effects of the information-driven awareness on epidemic spreading on multiplex networks.

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a "double-edged sword" effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

Epidemiology and Genetic Analysis of SARS-CoV-2 in Myanmar during the Community Outbreaks in 2020.

We aimed to analyze the situation of the first two epidemic waves in Myanmar using the publicly available daily situation of COVID-19 and whole-genome sequencing data of SARS-CoV-2. From March 23 to December 31, 2020, there were 33,917 confirmed cases and 741 deaths in Myanmar (case fatality rate of 2.18%). The first wave in Myanmar from March to July was linked to overseas travel, and then a second wave started from Rakhine State, a western border state, leading to the second wave spreading countrywide in Myanmar from August to December 2020. The estimated effective reproductive number (Rt) nationwide reached 6-8 at the beginning of each wave and gradually decreased as the epidemic spread to the community. The whole-genome analysis of 10 Myanmar SARS-CoV-2 strains together with 31 previously registered strains showed that the first wave was caused by GISAID clade O or PANGOLIN lineage B.6 and the second wave was changed to clade GH or lineage B.1.36.16 with a close genetic relationship with other South Asian strains. Constant monitoring of epidemiological situations combined with SARS-CoV-2 genome analysis is important for adjusting public health measures to mitigate the community transmissions of COVID-19.

VSHR: A Mathematical Model for the Prediction of Second-Wave COVID-19 Epidemics in Malaysia.

Since December 2019, a novel coronavirus (COVID-19) has spread all over the world, causing unpredictable economic losses and public fear. Although vaccines against this virus have been developed and administered for months, many countries still suffer from secondary COVID-19 infections, including the United Kingdom, France, and Malaysia. Observations of COVID-19 infections in the United Kingdom and France and their governance measures showed a certain number of similarities. A further investigation of these countries' COVID-19 transmission patterns suggested that when a turning point appeared, the values of their stringency indices per population density (PSI) were nearly proportional to their absolute infection rate (AIR). To justify our assumptions, we developed a mathematical model named VSHR to predict the COVID-19 turning point for Malaysia. VSHR was first trained on 30-day infection records prior to the United Kingdom, Germany, France, and Belgium's known turning points. It was then transferred to Malaysian COVID-19 data to predict this nation's turning point. Given the estimated AIR parameter values in 5 days, we were now able to locate the turning point's appearance on June 2nd, 2021. VSHR offered two improvements: (1) gathered countries into groups based on their SI patterns and (2) generated a model to identify the turning point for a target country within 5 days with 90% CI. Our research on COVID-19's turning point for a country is beneficial for governments and clinical systems against future COVID-19 infections.

Emerging dynamics from high-resolution spatial numerical epidemics.

Simulating nationwide realistic individual movements with a detailed geographical structure can help optimise public health policies. However, existing tools have limited resolution or can only account for a limited number of agents. We introduce Epidemap, a new framework that can capture the daily movement of more than 60 million people in a country at a building-level resolution in a realistic and computationally efficient way. By applying it to the case of an infectious disease spreading in France, we uncover hitherto neglected effects, such as the emergence of two distinct peaks in the daily number of cases or the importance of local density in the timing of arrival of the epidemic. Finally, we show that the importance of super-spreading events strongly varies over time.

Clinical management and outcome differences between first and second waves among COVID-19 hospitalized patients: A regional prospective observational cohort.

The objective was to describe the clinical characteristics and outcomes of hospitalized COVID-19 patients during the two different epidemic periods. Prospective, observational, cohort study of hospitalized COVID-19. A total of 421 consecutive patients were included, 188 during the first period (March-May 2020) and 233 in the second wave (July-December 2020). Clinical, epidemiological, prognostic and therapeutic data were compared. Patients of the first outbreak were older and more comorbid, presented worse PaO2/FiO2 ratio and an increased creatinine and D-dimer levels at hospital admission. The hospital stay was shorter (14.5[8;29] vs 8[6;14] days, p<0.001), ICU admissions (31.9% vs 13.3%, p<0.001) and the number of patients who required mechanical ventilation (OR = 0.12 [0.05-10.26]; p<0.001) were reduced. There were no significant differences in hospital and 30-day after discharge mortality (adjusted HR = 1.56; p = 0.1056) or hospital readmissions. New treatments and clinical strategies appear to improve hospital length, ICU admissions and the requirement for mechanical ventilation. However, we did not observe differences in mortality or readmissions.

Recommended reporting items for epidemic forecasting and prediction research: The EPIFORGE 2020 guidelines.

BACKGROUND: The importance of infectious disease epidemic forecasting and prediction research is underscored by decades of communicable disease outbreaks, including COVID-19. Unlike other fields of medical research, such as clinical trials and systematic reviews, no reporting guidelines exist for reporting epidemic forecasting and prediction research despite their utility. We therefore developed the EPIFORGE checklist, a guideline for standardized reporting of epidemic forecasting research. METHODS AND FINDINGS: We developed this checklist using a best-practice process for development of reporting guidelines, involving a Delphi process and broad consultation with an international panel of infectious disease modelers and model end users. The objectives of these guidelines are to improve the consistency, reproducibility, comparability, and quality of epidemic forecasting reporting. The guidelines are not designed to advise scientists on how to perform epidemic forecasting and prediction research, but rather to serve as a standard for reporting critical methodological details of such studies. CONCLUSIONS: These guidelines have been submitted to the EQUATOR network, in addition to hosting by other dedicated webpages to facilitate feedback and journal endorsement.

Switchover phenomenon induced by epidemic seeding on geometric networks.

It is a fundamental question in disease modeling how the initial seeding of an epidemic, spreading over a network, determines its final outcome. One important goal has been to find the seed configuration, which infects the most individuals. Although the identified optimal configurations give insight into how the initial state affects the outcome of an epidemic, they are unlikely to occur in real life. In this paper we identify two important seeding scenarios, both motivated by historical data, that reveal a complex phenomenon. In one scenario, the seeds are concentrated on the central nodes of a network, while in the second one, they are spread uniformly in the population. Comparing the final size of the epidemic started from these two initial conditions through data-driven and synthetic simulations on real and modeled geometric metapopulation networks, we find evidence for a switchover phenomenon: When the basic reproduction number [Formula: see text] is close to its critical value, more individuals become infected in the first seeding scenario, but for larger values of [Formula: see text], the second scenario is more dangerous. We find that the switchover phenomenon is amplified by the geometric nature of the underlying network and confirm our results via mathematically rigorous proofs, by mapping the network epidemic processes to bond percolation. Our results expand on the previous finding that, in the case of a single seed, the first scenario is always more dangerous and further our understanding of why the sizes of consecutive waves of a pandemic can differ even if their epidemic characters are similar.

Epidemiological features and spatial-temporal distribution of visceral leishmaniasis in mainland China: a population-based surveillance study from 2004 to 2019.

BACKGROUND: Although visceral leishmaniasis (VL) was largely brought under control in most regions of China during the previous century, VL cases have rebounded in western and central China in recent decades. The aim of this study was to investigate the epidemiological features and spatial-temporal distribution of VL in mainland China from 2004 to 2019. METHODS: Incidence and mortality data for VL during the period 2004-2019 were collected from the Public Health Sciences Data Center of China and annual national epidemic reports of VL, whose data source was the National Diseases Reporting Information System. Joinpoint regression analysis was performed to explore the trends of VL. Spatial autocorrelation and spatial-temporal clustering analysis were conducted to identify the distribution and risk areas of VL transmission. RESULTS: A total of 4877 VL cases were reported in mainland China during 2004-2019, with mean annual incidence of 0.0228/100,000. VL incidence showed a decreasing trend in general during our study period (annual percentage change [APC] = -4.2564, 95% confidence interval [CI]: -8.0856 to -0.2677). Among mainly endemic provinces, VL was initially heavily epidemic in Gansu, Sichuan, and especially Xinjiang, but subsequently decreased considerably. In contrast, Shaanxi and Shanxi witnessed significantly increasing trends, especially in 2017-2019. The first-level spatial-temporal aggregation area covered two endemic provinces in northwestern China, including Gansu and Xinjiang, with the gathering time from 2004 to 2011 (relative risk [RR] = 13.91, log-likelihood ratio [LLR] = 3308.87, P < 0.001). The secondary aggregation area was detected in Shanxi province of central China, with the gathering time of 2019 (RR = 1.61, LLR = 4.88, P = 0.041). The epidemic peak of October to November disappeared in 2018-2019, leaving only one peak in March to May. CONCLUSIONS: Our findings suggest that VL is still an important endemic infectious disease in China. Epidemic trends in different provinces changed significantly and spatial-temporal aggregation areas shifted from northwestern to central China during our study period. Mitigation strategies, including large-scale screening, insecticide spraying, and health education encouraging behavioral change, in combination with other integrated approaches, are needed to decrease transmission risk in areas at risk, especially in Shanxi, Shaanxi, and Gansu provinces.

Improved estimation of time-varying reproduction numbers at low case incidence and between epidemic waves.

We construct a recursive Bayesian smoother, termed EpiFilter, for estimating the effective reproduction number, R, from the incidence of an infectious disease in real time and retrospectively. Our approach borrows from Kalman filtering theory, is quick and easy to compute, generalisable, deterministic and unlike many current methods, requires no change-point or window size assumptions. We model R as a flexible, hidden Markov state process and exactly solve forward-backward algorithms, to derive R estimates that incorporate all available incidence information. This unifies and extends two popular methods, EpiEstim, which considers past incidence, and the Wallinga-Teunis method, which looks forward in time. We find that this combination of maximising information and minimising assumptions significantly reduces the bias and variance of R estimates. Moreover, these properties make EpiFilter more statistically robust in periods of low incidence, where several existing methods can become destabilised. As a result, EpiFilter offers improved inference of time-varying transmission patterns that are advantageous for assessing the risk of upcoming waves of infection or the influence of interventions, in real time and at various spatial scales.

Assessing the Impacts of COVID-19 on the Industrial Sectors and Economy of China.

Since December 2019, the COVID-19 epidemic has been spreading continuously in China and many countries in the world, causing widespread concern among the whole society. To cope with the epidemic disaster, most provinces and cities in China have adopted prevention and control measures such as home isolation, blocking transportation, and extending the Spring Festival holiday, which has caused a serious impact on China's output of various sectors, international trade, and labor employment, ultimately generating great losses to the Chinese economic system in 2020. But how big is the loss? How can we assess this for a country? At present, there are few analyses based on quantitative models to answer these important questions. In the following, we describe a quantitative-based approach of assessing the potential impact of the COVID-19 epidemic on the economic system and the sectors taking China as the base case. The proposed approach can provide timely data and quantitative tools to support the complex decision-making process that government agencies (and the private sector) need to manage to respond to this tragic epidemic and maintain stable economic development. Based on the available data, this article proposes a hypothetical scenario and then adopts the Computable General Equilibrium (CGE) model to calculate the comprehensive economic losses of the epidemic from the aspects of the direct shock on the output of seriously affected sectors, international trade, and labor force. The empirical results show that assuming a GDP growth rate of 4-8% in the absence of COVID-19, GDP growth in 2020 would be -8.77 to -12.77% after the COVID-19. Companies and activities associated with transportation and service sectors are among the most impacted, and companies and supply chains related to the manufacturing subsector lead the economic losses. Finally, according to the calculation results, the corresponding countermeasures and suggestions are put forward: disaster recovery for key sectors such as the labor force, transportation sector, and service sectors should be enhanced; disaster emergency rescue work in highly sensitive sectors should be carried out; in the long run, precise measures to strengthen the refined management of disaster risk with big data resources and means should be taken.

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).

A novel geo-hierarchical population mobility model for spatial spreading of resurgent epidemics.

Computational models for large, resurgent epidemics are recognized as a crucial tool for predicting the spread of infectious diseases. It is widely agreed, that such models can be augmented with realistic multiscale population models and by incorporating human mobility patterns. Nevertheless, a large proportion of recent studies, aimed at better understanding global epidemics, like influenza, measles, H1N1, SARS, and COVID-19, underestimate the role of heterogeneous mixing in populations, characterized by strong social structures and geography. Motivated by the reduced tractability of studies employing homogeneous mixing, which make conclusions hard to deduce, we propose a new, very fine-grained model incorporating the spatial distribution of population into geographical settlements, with a hierarchical organization down to the level of households (inside which we assume homogeneous mixing). In addition, population is organized heterogeneously outside households, and we model the movement of individuals using travel distance and frequency parameters for inter- and intra-settlement movement. Discrete event simulation, employing an adapted SIR model with relapse, reproduces important qualitative characteristics of real epidemics, like high variation in size and temporal heterogeneity (e.g., waves), that are challenging to reproduce and to quantify with existing measures. Our results pinpoint an important aspect, that epidemic size is more sensitive to the increase in distance of travel, rather that the frequency of travel. Finally, we discuss implications for the control of epidemics by integrating human mobility restrictions, as well as progressive vaccination of individuals.

Alternative Strategies for the Estimation of a Disease's Basic Reproduction Number: A Model-Agnostic Study.

This work presents a model-agnostic evaluation of four different models that estimate a disease's basic reproduction number. The evaluation presented is twofold: first, the theory behind each of the models is reviewed and compared; then, each model is tested with eight impartial simulations. All scenarios were constructed in an experimental framework that allows each model to fulfill its assumptions and hence, obtain unbiased results for each case. Among these models is the one proposed by Thompson et al. (Epidemics 29:100356, 2019), i.e., a Bayesian estimation method well established in epidemiological practice. The other three models include a novel state-space method and two simulation-based approaches based on a Poisson infection process. The advantages and flaws of each model are discussed from both theoretical and practical standpoints. Finally, we present the evolution of Covid-19 outbreak in Colombia as a case study for computing the basic reproduction number with each one of the reviewed methods.

Contact tracing efficiency, transmission heterogeneity, and accelerating COVID-19 epidemics.

Simultaneously controlling COVID-19 epidemics and limiting economic and societal impacts presents a difficult challenge, especially with limited public health budgets. Testing, contact tracing, and isolating/quarantining is a key strategy that has been used to reduce transmission of SARS-CoV-2, the virus that causes COVID-19 and other pathogens. However, manual contact tracing is a time-consuming process and as case numbers increase a smaller fraction of cases' contacts can be traced, leading to additional virus spread. Delays between symptom onset and being tested (and receiving results), and a low fraction of symptomatic cases being tested and traced can also reduce the impact of contact tracing on transmission. We examined the relationship between increasing cases and delays and the pathogen reproductive number Rt, and the implications for infection dynamics using deterministic and stochastic compartmental models of SARS-CoV-2. We found that Rt increased sigmoidally with the number of cases due to decreasing contact tracing efficacy. This relationship results in accelerating epidemics because Rt initially increases, rather than declines, as infections increase. Shifting contact tracers from locations with high and low case burdens relative to capacity to locations with intermediate case burdens maximizes their impact in reducing Rt (but minimizing total infections may be more complicated). Contact tracing efficacy decreased sharply with increasing delays between symptom onset and tracing and with lower fraction of symptomatic infections being tested. Finally, testing and tracing reductions in Rt can sometimes greatly delay epidemics due to the highly heterogeneous transmission dynamics of SARS-CoV-2. These results demonstrate the importance of having an expandable or mobile team of contact tracers that can be used to control surges in cases. They also highlight the synergistic value of high capacity, easy access testing and rapid turn-around of testing results, and outreach efforts to encourage symptomatic cases to be tested immediately after symptom onset.

The epidemic burden on prisons in Lombardy (Italy) during the first wave of the COVID-19 pandemic: the transmission and measures of prevention in detention facilities.

PURPOSE: Early on in the COVID-19 pandemic, the scientific community highlighted a potential risk of epidemics occurring inside prisons. Consequently, specific operational guidelines were promptly released, and containment measures were quickly implemented in prisons. This paper aims to describe the spread of COVID-19 in detention facilities within the Lombardy region of Italy during March to July 2020, and the impact of the prevention and control measures implemented. DESIGN/METHODOLOGY/APPROACH: A descriptive retrospective analysis of case distribution was performed for all COVID-19 cases identified among people in detention (PiD) and prison officers (POs). A comparison of the epidemic burden affecting different populations and a correlation analysis between the number of cases that occurred and prevention measures implemented were also carried out. FINDINGS: From this study, it emerged that POs were at a high risk of contracting COVID-19. This study observed a delay in the occurrence of cases among PiD and substantial heterogeneity in the size of outbreaks across different prisons. Correlation between reported cases among PiD and registered sick leave taken by POs suggested the latter contributed to introducing the infection into prison settings. Finally, number of cases among PiD inversely correlated with the capacity of each prison to identify and set up dedicated areas for medical isolation. ORIGINALITY/VALUE: Prevention and control measures when adopted in a timely manner were effective in protecting PiD. According to the findings, POs are a population at high risk for acquiring and transmitting COVID-19 and should be prioritized for testing, active case finding and vaccination. This study highlights the critical importance of including prison settings within emergency preparedness plans.

Estimating the effects of non-pharmaceutical interventions on the number of new infections with COVID-19 during the first epidemic wave.

The novel coronavirus (SARS-CoV-2) has rapidly developed into a global epidemic. To control its spread, countries have implemented non-pharmaceutical interventions (NPIs), such as school closures, bans of small gatherings, or even stay-at-home orders. Here we study the effectiveness of seven NPIs in reducing the number of new infections, which was inferred from the reported cases of COVID-19 using a semi-mechanistic Bayesian hierarchical model. Based on data from the first epidemic wave of n = 20 countries (i.e., the United States, Canada, Australia, the EU-15 countries, Norway, and Switzerland), we estimate the relative reduction in the number of new infections attributed to each NPI. Among the NPIs considered, bans of large gatherings were most effective, followed by venue and school closures, whereas stay-at-home orders and work-from-home orders were least effective. With this retrospective cross-country analysis, we provide estimates regarding the effectiveness of different NPIs during the first epidemic wave.