Adherence and sustainability of interventions informing optimal control against the COVID-19 pandemic

Is a tiered restrictions system an effective intervention for COVID-19 control? Results from Portugal, November-December 2020

BACKGROUND

In November 2020, similar to other European countries, Portugal implemented a tiered restrictions system to control the COVID-19 pandemic. We aimed to compare the COVID-19 growth rate across tiers to assess the effect of a tiered restrictions system in Portugal, using models with different times between tiers assessment. Our hypothesis was that being in a higher tier brings a faster deceleration in the growth rate than being in a lower tier.

METHODS

The national database of notified COVID-19 cases and publicly available data were used to analyse the effect of the tiered restrictions system on the COVID-19 incidence growth rate. The tiers were based on the European Centre for Disease Control risk classification: moderate, high, very and extremely high. We used a generalised mixed-effects regression model to estimate the growth rate ratio (GRR) for each tier, comparing the growth rates of higher tiers using moderate tier as reference. Three models were fitted using different times between tiers assessment, separated by 14 days.

RESULTS

We included 156 034 cases. Very high tier was the most frequent combination in all the three moments assessed (21.2%), and almost 50% of the municipalities never changed tier during the study period. Immediately after the tiers implementation, a reduction was identified in the municipalities in high tier (GRR high tier: 0.90 [95%CI: 0.79; 1.02]) and very high tier (GRR very high tier: 0.68 [95%CI: 0.61; 0.77]), however with some imprecision in the 95% confidence interval for the high tier. A reduction in very high tier growth rate was identified two weeks (GRR: 0.79 [95%CI: 0.71; 0.88]) and four weeks (GRR: 0.77 [95%CI: 0.74; 0.82]) after the implementation, compared to moderate tier. In high tier, a reduction was also identified in both times, although smaller.

CONCLUSIONS

We observed a reduction in the growth rate in very high tier after the tiered restriction system was implemented, but we also observed a lag between tiered restriction system implementation and the onset of consequent effects. This could suggest the importance of early implementation of stricter measures for pandemic control. Thus, studies analysing a broader period of time are needed.

Drivers and impact of the early silent invasion of SARS-CoV-2 Alpha

SARS-CoV-2 variants of concern (VOCs) circulated cryptically before being identified as a threat, delaying interventions. Here we studied the drivers of such silent spread and its epidemic impact to inform future response planning. We focused on Alpha spread out of the UK. We integrated spatio-temporal records of international mobility, local epidemic growth and genomic surveillance into a Bayesian framework to reconstruct the first three months after Alpha emergence. We found that silent circulation lasted from days to months and decreased with the logarithm of sequencing coverage. Social restrictions in some countries likely delayed the establishment of local transmission, mitigating the negative consequences of late detection. Revisiting the initial spread of Alpha supports local mitigation at the destination in case of emerging events.

SARS-CoV-2 infection prevalence and associated factors among primary healthcare workers in France after the third COVID-19 wave

Data on the SARS-CoV-2 infection among primary health care workers (PHCWs) are scarce but essential to reflect on policy regarding prevention and control measures. We assessed the prevalence of PHCWs who have been infected by SARS-CoV-2 in comparison with modeling from the general population in metropolitan France, and associated factors. A cross-sectional study was conducted among general practitioners (GPs), pediatricians, dental and pharmacy workers in primary care between May and August 2021. Participants volunteered to provide a dried-blood spot for SARS-CoV-2 antibody assessment and completed a questionnaire. The primary outcome was defined as the detection of infection-induced antibodies (anti-nucleocapsid IgG, and for non-vaccinees: anti-Spike IgG and neutralizing antibodies) or previous self-reported infection (positive RT-qPCR or antigenic test, or positive ELISA test before vaccination). Estimates were adjusted using weights for representativeness and compared with prediction from the general population. Poisson regressions were used to quantify associated factors. The analysis included 1612 PHCWs. Weighted prevalences were: 31.7% (95% CI 27.5-36.0) for GPs, 28.7% (95% CI 24.4-33.0) for pediatricians, 25.2% (95% CI 20.6-31.0) for dentists, and 25.5% (95% CI 18.2-34.0) for pharmacists. Estimates were compatible with model predictions for the general population. PHCWs more likely to be infected were: GPs compared to pharmacist assistants (adjusted prevalence ratio [aPR] = 2.26; CI 95% 1.01-5.07), those living in Île-de-France (aPR = 1.53; CI 95% 1.14-2.05), South-East (aPR = 1.57; CI 95% 1.19-2.08), North-East (aPR = 1.81; CI 95% 1.38-2.37), and those having an unprotected contact with a COVID-19 case within the household (aPR = 1.48; CI 95% 1.22-1.80). Occupational factors were not associated with infection. In conclusion, the risk of SARS-CoV-2 exposure for PHCWs was more likely to have occurred in the community rather than at their workplace.

Predictive models for health outcomes due to SARS-CoV-2, including the effect of vaccination: a systematic review

BACKGROUND

The interaction between modelers and policymakers is becoming more common due to the increase in computing speed seen in recent decades. The recent pandemic caused by the SARS-CoV-2 virus was no exception. Thus, this study aims to identify and assess epidemiological mathematical models of SARS-CoV-2 applied to real-world data, including immunization for coronavirus 2019 (COVID-19).

METHODOLOGY

PubMed, JSTOR, medRxiv, LILACS, EconLit, and other databases were searched for studies employing epidemiological mathematical models of SARS-CoV-2 applied to real-world data. We summarized the information qualitatively, and each article included was assessed for bias risk using the Joanna Briggs Institute (JBI) and PROBAST checklist tool. The PROSPERO registration number is CRD42022344542.

FINDINGS

In total, 5646 articles were retrieved, of which 411 were included. Most of the information was published in 2021. The countries with the highest number of studies were the United States, Canada, China, and the United Kingdom; no studies were found in low-income countries. The SEIR model (susceptible, exposed, infectious, and recovered) was the most frequently used approach, followed by agent-based modeling. Moreover, the most commonly used software were R, Matlab, and Python, with the most recurring health outcomes being death and recovery. According to the JBI assessment, 61.4% of articles were considered to have a low risk of bias.

INTERPRETATION

The utilization of mathematical models increased following the onset of the SARS-CoV-2 pandemic. Stakeholders have begun to incorporate these analytical tools more extensively into public policy, enabling the construction of various scenarios for public health. This contribution adds value to informed decision-making. Therefore, understanding their advancements, strengths, and limitations is essential.

The impact of spatial connectivity on NPIs effectiveness

BACKGROUND

France implemented a combination of non-pharmaceutical interventions (NPIs) to manage the COVID-19 pandemic between September 2020 and June 2021. These included a lockdown in the fall 2020 - the second since the start of the pandemic - to counteract the second wave, followed by a long period of nighttime curfew, and by a third lockdown in the spring 2021 against the Alpha wave. Interventions have so far been evaluated in isolation, neglecting the spatial connectivity between regions through mobility that may impact NPI effectiveness.

METHODS

Focusing on September 2020-June 2021, we developed a regionally-based epidemic metapopulation model informed by observed mobility fluxes from daily mobile phone data and fitted the model to regional hospital admissions. The model integrated data on vaccination and variants spread. Scenarios were designed to assess the impact of the Alpha variant, characterized by increased transmissibility and risk of hospitalization, of the vaccination campaign and alternative policy decisions.

RESULTS

The spatial model better captured the heterogeneity observed in the regional dynamics, compared to models neglecting inter-regional mobility. The third lockdown was similarly effective to the second lockdown after discounting for immunity, Alpha, and seasonality (51% vs 52% median regional reduction in the reproductive number R0, respectively). The 6pm nighttime curfew with bars and restaurants closed, implemented in January 2021, substantially reduced COVID-19 transmission. It initially led to 49% median regional reduction of R0, decreasing to 43% reduction by March 2021. In absence of vaccination, implemented interventions would have been insufficient against the Alpha wave. Counterfactual scenarios proposing a sequence of lockdowns in a stop-and-go fashion would have reduced hospitalizations and restriction days for low enough thresholds triggering and lifting restrictions.

CONCLUSIONS

Spatial connectivity induced by mobility impacted the effectiveness of interventions especially in regions with higher mobility rates. Early evening curfew with gastronomy sector closed allowed authorities to delay the third wave. Stop-and-go lockdowns could have substantially lowered both healthcare and societal burdens if implemented early enough, compared to the observed application of lockdown-curfew-lockdown, but likely at the expense of several labor sectors. These findings contribute to characterize the effectiveness of implemented strategies and improve pandemic preparedness.

Transmission dynamics informed neural network with application to COVID-19 infections

Since the end of 2019 the COVID-19 repeatedly surges with most countries/territories experiencing multiple waves, and mechanism-based epidemic models played important roles in understanding the transmission mechanism of multiple epidemic waves. However, capturing temporal changes of the transmissibility of COVID-19 during the multiple waves keeps ill-posed problem for traditional mechanism-based epidemic compartment models, because that the transmission rate is usually assumed to be specific piecewise functions and more parameters are added to the model once multiple epidemic waves involved, which poses a huge challenge to parameter estimation. Meanwhile, data-driven deep neural networks fail to discover the driving factors of repeated outbreaks and lack interpretability. In this study, aiming at developing a data-driven method to project time-dependent parameters but also merging the advantage of mechanism-based models, we propose a transmission dynamics informed neural network (TDINN) by encoding the SEIRD compartment model into deep neural networks. We show that the proposed TDINN algorithm performs very well when fitting the COVID-19 epidemic data with multiple waves, where the epidemics in the United States, Italy, South Africa, and Kenya, and several outbreaks the Omicron variant in China are taken as examples. In addition, the numerical simulation shows that the trained TDINN can also perform as a predictive model to capture the future development of COVID-19 epidemic. We find that the transmission rate inferred by the TDINN frequently fluctuates, and a feedback loop between the epidemic shifting and the changes of transmissibility drives the occurrence of multiple waves. We observe a long response delay to the implementation of control interventions in the four countries, while the decline of the transmission rate in the outbreaks in China usually happens once the implementation of control interventions. The further simulation show that 17 days' delay of the response to the implementation of control interventions lead to a roughly four-fold increase in daily reported cases in one epidemic wave in Italy, which suggest that a rapid response to policies that strengthen control interventions can be effective in flattening the epidemic curve or avoiding subsequent epidemic waves. We observe that the transmission rate in the outbreaks in China is already decreasing before enhancing control interventions, providing the evidence that the increasing of the epidemics can drive self-conscious behavioural changes to protect against infections.

Determinants of COVID-19 vaccination worldwide: WORLDCOV, a retrospective observational study

Introduction

The COVID-19 pandemic has resulted in numerous deaths, great suffering, and significant changes in people's lives worldwide. The introduction of the vaccines was a light in the darkness, but after 18 months, a great disparity in vaccination coverage between countries has been observed. As disparities in vaccination coverage have become a global public health issue, this study aimed to analyze several variables to identify possible determinants of COVID-19 vaccination.

Methods

An ecological study was conducted using pooled secondary data sourced from institutional sites. A total of 205 countries and territories worldwide were included. A total of 16 variables from different fields were considered to establish possible determinants of COVID-19 vaccination: sociodemographic, cultural, infrastructural, economic and political variables, and health system performance indicators. The percentage of the population vaccinated with at least one dose and the total doses administered per 100 residents on 15 June 2022 were identified as indicators of vaccine coverage and outcomes. Raw and adjusted values for delivered vaccine doses in the multivariate GLM were determined using R. The tested hypothesis (i.e., variables as determinants of COVID-19 vaccination) was formulated before data collection. The study protocol was registered with the grant number NCT05471635.

Results

GDP per capita [odds = 1.401 (1.299-1.511) CI 95%], access to electricity [odds = 1.625 (1.559-1.694) CI 95%], political stability, absence of violence/terrorism [odds = 1.334 (1.284-1.387) CI 95%], and civil liberties [odds = 0.888 (0.863-0.914) CI 95%] were strong determinants of COVID-19 vaccination. Several other variables displayed a statistically significant association with outcomes, although the associations were stronger for total doses administered per 100 residents. There was a substantial overlap between raw outcomes and their adjusted counterparts.

Discussion

This pioneering study is the first to analyze the association between several different categories of indicators and COVID-19 vaccination coverage in a wide complex setting, identifying strong determinants of vaccination coverage. Political decision-makers should consider these findings when organizing mass vaccination campaigns in a pandemic context to reduce inequalities between nations and to achieve a common good from a public health perspective.

Prevalence of long COVID decreases for increasing COVID-19 vaccine uptake

Long COVID is a post-COVID-19 condition characterized by persistent symptoms that can develop after SARS-CoV-2 infection. Estimating and comparing its prevalence across countries is difficult, hindering the quantitative assessment of massive vaccination campaigns as a preventive measure. By integrating epidemiological, demographic and vaccination data, we first reconcile the estimates of long COVID prevalence in the U.K. and the U.S., and estimate a 7-fold yearly increase in the global median prevalence between 2020 and 2022. Second, we estimate that vaccines against COVID-19 decrease the prevalence of long COVID among U.S. adults by 20.9% (95% CI: -32.0%, -9.9%) and, from the analysis of 158 countries, by -15.7% (95% CI: -18.0%, -13.4%) among all who had COVID-19. Our population-level analysis complements the current knowledge from patients data and highlights how aggregated data from fully operational epidemic surveillance and monitoring can inform about the potential impact of long COVID on national and global public health in the next future.

Enhancing global preparedness during an ongoing pandemic from partial and noisy data

As the coronavirus disease 2019 spread globally, emerging variants such as B.1.1.529 quickly became dominant worldwide. Sustained community transmission favors the proliferation of mutated sub-lineages with pandemic potential, due to cross-national mobility flows, which are responsible for consecutive cases surge worldwide. We show that, in the early stages of an emerging variant, integrating data from national genomic surveillance and global human mobility with large-scale epidemic modeling allows to quantify its pandemic potential, providing quantifiable indicators for pro-active policy interventions. We validate our framework on worldwide spreading variants and gain insights about the pandemic potential of BA.5, BA.2.75, and other sub- and lineages. We combine the different sources of information in a simple estimate of the pandemic delay and show that only in combination, the pandemic potentials of the lineages are correctly assessed relative to each other. Compared to a country-level epidemic intelligence, our scalable integrated approach, that is pandemic intelligence, permits to enhance global preparedness to contrast the pandemic of respiratory pathogens such as SARS-CoV-2.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France

BACKGROUND

Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.

METHODS

We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.

FINDINGS

Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.

INTERPRETATION

Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Global patterns and drivers of influenza decline during the COVID-19 pandemic

OBJECTIVES

The influenza circulation reportedly declined during the COVID-19 pandemic in many countries. The occurrence of this change has not been studied worldwide nor its potential drivers.

METHODS

The change in the proportion of positive influenza samples reported by country and trimester was computed relative to the 2014-2019 period using the FluNet database. Random forests were used to determine predictors of change from demographical, weather, pandemic preparedness, COVID-19 incidence, and pandemic response characteristics. Regression trees were used to classify observations according to these predictors.

RESULTS

During the COVID-19 pandemic, the influenza decline relative to prepandemic levels was global but heterogeneous across space and time. It was more than 50% for 311 of 376 trimesters-countries and even more than 99% for 135. COVID-19 incidence and pandemic preparedness were the two most important predictors of the decline. Europe and North America initially showed limited decline despite high COVID-19 restrictions; however, there was a strong decline afterward in most temperate countries, where pandemic preparedness, COVID-19 incidence, and social restrictions were high; the decline was limited in countries where these factors were low. The "zero-COVID" countries experienced the greatest decline.

CONCLUSION

Our findings set the stage for interpreting the resurgence of influenza worldwide.

Proactive vs. reactive country responses to the COVID-19 pandemic shock

The infection caused by SARS-CoV-2, responsible for the COVID-19 pandemic, is characterized by an infectious period with either asymptomatic or pre-symptomatic phases, leading to a rapid surge of mild and severe cases putting national health systems under serious stress. To avoid their collapse, and in the absence of pharmacological treatments, during the early pandemic phase countries worldwide were forced to adopt strategies, from elimination to mitigation, based on non-pharmacological interventions which, in turn, overloaded social, educational and economic systems. To date, the heterogeneity and incompleteness of data sources does not allow to quantify the multifaceted impact of the pandemic at country level and, consequently, to compare the effectiveness of country responses. Here, we tackle this challenge from a complex systems perspective, proposing a model to evaluate the impact of systemic failures in response to the pandemic shock. We use health, behavioral and economic indicators for 44 countries to build a shock index quantifying responses in terms of robustness and resilience, highlighting the crucial advantage of proactive policy and decision making styles over reactive ones, which can be game-changing during the emerging of a new variant of concern.

Transmission Dynamics and Effectiveness of Control Measures during COVID-19 Surge, Taiwan, April-August 2021

An unprecedented surge of COVID-19 cases in Taiwan in May 2021 led the government to implement strict nationwide control measures beginning May 15. During the surge, the government was able to bring the epidemic under control without a complete lockdown despite the cumulative case count reaching >14,400 and >780 deaths. We investigated the effectiveness of the public health and social measures instituted by the Taiwan government by quantifying the change in the effective reproduction number, which is a summary measure of the ability of the pathogen to spread through the population. The control measures that were instituted reduced the effective reproduction number from 2.0-3.3 to 0.6-0.7. This decrease was correlated with changes in mobility patterns in Taiwan, demonstrating that public compliance, active case finding, and contact tracing were effective measures in preventing further spread of the disease.

Non-pharmaceutical Interventions and Social Distancing as Intersubjective Care and Collective Protection

The paper discusses non-pharmaceutical interventions (NPIs) as a collective form of protection that, in terms of health justice, benefits groups at risk, allowing them to engage in social life and activities during health crises. More specifically, the paper asserts that NPIs that realize social distancing are justifiable insofar as they are constitutive of a type of social protection that allows everyone, especially social disadvantaged agents, to access the public health sphere and other fundamental social spheres, such as the family and civil society.

Sideward contact tracing and the control of epidemics in large gatherings

Effective contact tracing is crucial to containing epidemic spreading without disrupting societal activities, especially during a pandemic. Large gatherings play a key role, potentially favouring superspreading events. However, the effects of tracing in large groups have not been fully assessed so far. We show that in addition to forward tracing, which reconstructs to whom the disease spreads, and backward tracing, which searches from whom the disease spreads, a third 'sideward' tracing is always present, when tracing gatherings. This is an indirect tracing that detects infected asymptomatic individuals, even if they have been neither directly infected by nor directly transmitted the infection to the index case. We analyse this effect in a model of epidemic spreading for SARS-CoV-2, within the framework of simplicial activity-driven temporal networks. We determine the contribution of the three tracing mechanisms to the suppression of epidemic spreading, showing that sideward tracing induces a non-monotonic behaviour in the tracing efficiency, as a function of the size of the gatherings. Based on our results, we suggest an optimal choice for the sizes of the gatherings to be traced and we test the strategy on an empirical dataset of gatherings on a university campus.