Impact of lockdown on COVID-19 epidemic in Île-de-France and possible exit strategies

Spatial scales of COVID-19 transmission in Mexico

During outbreaks of emerging infectious diseases, internationally connected cities often experience large and early outbreaks, while rural regions follow after some delay. This hierarchical structure of disease spread is influenced primarily by the multiscale structure of human mobility. However, during the COVID-19 epidemic, public health responses typically did not take into consideration the explicit spatial structure of human mobility when designing nonpharmaceutical interventions (NPIs). NPIs were applied primarily at national or regional scales. Here, we use weekly anonymized and aggregated human mobility data and spatially highly resolved data on COVID-19 cases at the municipality level in Mexico to investigate how behavioral changes in response to the pandemic have altered the spatial scales of transmission and interventions during its first wave (March-June 2020). We find that the epidemic dynamics in Mexico were initially driven by exports of COVID-19 cases from Mexico State and Mexico City, where early outbreaks occurred. The mobility network shifted after the implementation of interventions in late March 2020, and the mobility network communities became more disjointed while epidemics in these communities became increasingly synchronized. Our results provide dynamic insights into how to use network science and epidemiological modeling to inform the spatial scale at which interventions are most impactful in mitigating the spread of COVID-19 and infectious diseases in general.

Spatio-temporal analysis of COVID-19 lockdown effect to survive in the US counties using ANN

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

An SEIR network epidemic model with manual and digital contact tracing allowing delays

We consider an SEIR epidemic model on a network also allowing random contacts, where recovered individuals could either recover naturally or be diagnosed. Upon diagnosis, manual contact tracing is triggered such that each infected network contact is reported, tested and isolated with some probability and after a random delay. Additionally, digital tracing (based on a tracing app) is triggered if the diagnosed individual is an app-user, and then all of its app-using infectees are immediately notified and isolated. The early phase of the epidemic with manual and/or digital tracing is approximated by different multi-type branching processes, and three respective reproduction numbers are derived. The effectiveness of both contact tracing mechanisms is numerically quantified through the reduction of the reproduction number. This shows that app-using fraction plays an essential role in the overall effectiveness of contact tracing. The relative effectiveness of manual tracing compared to digital tracing increases if: more of the transmission occurs on the network, when the tracing delay is shortened, and when the network degree distribution is heavy-tailed. For realistic values, the combined tracing case can reduce R0 by 20%-30%, so other preventive measures are needed to reduce the reproduction number down to 1.2-1.4 for contact tracing to make it successful in avoiding big outbreaks.

Human movement avoidance decisions during Coronavirus disease 2019 in Japan

Understanding host behavioral change in response to epidemics is important to forecast the disease dynamics. To predict the behavioral change relevant to the epidemic situation (e.g., the number of reported cases), we need to know the epidemic situation at the moment of decision, which is difficult to identify from the records of actually performed human mobility. In this study, the largest travel accommodation reservation data covering half of the existed accommodations in Japan was analyzed to observe decision-making timings and how it responded to the changing epidemic situation during Japan's Coronavirus Disease 2019 until February 2023. To this end, we measured mobility avoidance index proposed in Ito et al., 2022 to indicate people's decision of mobility avoidance and quantified it using the time-series of the accommodation booking/cancellation data. We observed matches of the peak dates of the mobility avoidance and the number of reported cases, and mobility avoidance changed proportional to the logarithmic number of reported cases. We also found that the slope of mobility avoidance against the change of the logarithmic number of reported cases were similar among the epidemic waves, while the intercept of that was much reduced as the first epidemic wave passed by. People measure the intensity of epidemic by logarithm of the number of reported cases. The sensitivity of their response is established during the first wave and the people's response became weakened after the first experience, as if the number of reported cases were multiplied by a constant small factor.

Using a population-based Kalman estimator to model the COVID-19 epidemic in France: estimating associations between disease transmission and non-pharmaceutical interventions

In response to the COVID-19 pandemic caused by SARS-CoV-2, governments have adopted a wide range of non-pharmaceutical interventions (NPI). These include stringent measures such as strict lockdowns, closing schools, bars and restaurants, curfews, and barrier gestures such as mask-wearing and social distancing. Deciphering the effectiveness of each NPI is critical to responding to future waves and outbreaks. To this end, we first develop a dynamic model of the French COVID-19 epidemics over a one-year period. We rely on a global extended Susceptible-Infectious-Recovered (SIR) mechanistic model of infection that includes a dynamic transmission rate over time. Multilevel data across French regions are integrated using random effects on the parameters of the mechanistic model, boosting statistical power by multiplying integrated observation series. We estimate the parameters using a new population-based statistical approach based on a Kalman filter, used for the first time in analysing real-world data. We then fit the estimated time-varying transmission rate using a regression model that depends on the NPIs while accounting for vaccination coverage, the occurrence of variants of concern (VoC), and seasonal weather conditions. We show that all NPIs considered have an independent significant association with transmission rates. In addition, we show a strong association between weather conditions that reduces transmission in summer, and we also estimate increased transmissibility of VoC.

Estimating SARS-CoV-2 infection probabilities with serological data and a Bayesian mixture model

The individual results of SARS-CoV-2 serological tests measured after the first pandemic wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus, these results are usually returned as a binary or ternary variable, relying on predefined cut-offs. We propose a Bayesian mixture model to estimate individual infection probabilities, based on 81,797 continuous anti-spike IgG tests from Euroimmun collected in France after the first wave. This approach used serological results as a continuous variable, and was therefore not based on diagnostic cut-offs. Cumulative incidence, which is necessary to compute infection probabilities, was estimated according to age and administrative region. In France, we found that a "negative" or a "positive" test, as classified by the manufacturer, could correspond to a probability of infection as high as 61.8% or as low as 67.7%, respectively. "Indeterminate" tests encompassed probabilities of infection ranging from 10.8 to 96.6%. Our model estimated tailored individual probabilities of SARS-CoV-2 infection based on age, region, and serological result. It can be applied in other contexts, if estimates of cumulative incidence are available.

Analysis and comparative study of a deterministic mathematical model of SARS-COV-2 with fractal-fractional operators: a case study

In this paper, we investigate a fractal-fractional-order mathematical model with the influence of hospitalized patients and the impact of vaccination with fractal-fractional operators. The respective derivatives are considered in the Caputo, Caputo Fabrizio, and Atangana-Baleanu senses of fractional order α and fractal dimension τ . For the proposed problem, some results regarding basic reproduction number and stability are given. Using the next-generation matrix approach, we have investigated the global and local stability of several types of equilibrium points. We provide a detailed analysis of the existence and uniqueness of the solution. Moreover, we fit the model with the real data of Pakistan from June 01, 2020, till March 24, 2021. Then, we use the fractal-fractional derivative to find a numerical solution for the model. MATLAB software is used for numerical illustration. Graphical presentations corresponding to different parameteric values are given as well.

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.

Exploring factors shaping antibiotic resistance patterns in Streptococcus pneumoniae during the 2020 COVID-19 pandemic

Non-pharmaceutical interventions implemented to block SARS-CoV-2 transmission in early 2020 led to global reductions in the incidence of invasive pneumococcal disease (IPD). By contrast, most European countries reported an increase in antibiotic resistance among invasive Streptococcus pneumoniae isolates from 2019 to 2020, while an increasing number of studies reported stable pneumococcal carriage prevalence over the same period. To disentangle the impacts of the COVID-19 pandemic on pneumococcal epidemiology in the community setting, we propose a mathematical model formalizing simultaneous transmission of SARS-CoV-2 and antibiotic-sensitive and -resistant strains of S. pneumoniae. To test hypotheses underlying these trends five mechanisms were built into the model and examined: (1) a population-wide reduction of antibiotic prescriptions in the community, (2) lockdown effect on pneumococcal transmission, (3) a reduced risk of developing an IPD due to the absence of common respiratory viruses, (4) community azithromycin use in COVID-19 infected individuals, (5) and a longer carriage duration of antibiotic-resistant pneumococcal strains. Among 31 possible pandemic scenarios involving mechanisms individually or in combination, model simulations surprisingly identified only two scenarios that reproduced the reported trends in the general population. They included factors (1), (3), and (4). These scenarios replicated a nearly 50% reduction in annual IPD, and an increase in antibiotic resistance from 20% to 22%, all while maintaining a relatively stable pneumococcal carriage. Exploring further, higher SARS-CoV-2 R0 values and synergistic within-host virus-bacteria interaction mechanisms could have additionally contributed to the observed antibiotic resistance increase. Our work demonstrates the utility of the mathematical modeling approach in unraveling the complex effects of the COVID-19 pandemic responses on AMR dynamics.

Incidence of mental health diagnoses during the COVID-19 pandemic: a multinational network study

AIMS

Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.

METHODS

By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.

RESULTS

A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.

CONCLUSIONS

Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.

Estimating the population effectiveness of interventions against COVID-19 in France: A modelling study

BACKGROUND

Non-pharmaceutical interventions (NPIs) and vaccines have been widely used to manage the COVID-19 pandemic. However, uncertainty persists regarding the effectiveness of these interventions due to data quality issues, methodological challenges, and differing contextual factors. Accurate estimation of their effects is crucial for future epidemic preparedness.

METHODS

To address this, we developed a population-based mechanistic model that includes the impact of NPIs and vaccines on SARS-CoV-2 transmission and hospitalization rates. Our statistical approach estimated all parameters in one step, accurately propagating uncertainty. We fitted the model to comprehensive epidemiological data in France from March 2020 to October 2021. With the same model, we simulated scenarios of vaccine rollout.

RESULTS

The first lockdown was the most effective, reducing transmission by 84 % (95 % confidence interval (CI) 83-85). Subsequent lockdowns had diminished effectiveness (reduction of 74 % (69-77) and 11 % (9-18), respectively). A 6 pm curfew was more effective than one at 8 pm (68 % (66-69) vs. 48 % (45-49) reduction), while school closures reduced transmission by 15 % (12-18). In a scenario without vaccines before November 2021, we predicted 159,000 or 168 % (95 % prediction interval (PI) 70-315) more deaths and 1,488,000 or 300 % (133-492) more hospitalizations. If a vaccine had been available after 100 days, over 71,000 deaths (16,507-204,249) and 384,000 (88,579-1,020,386) hospitalizations could have been averted.

CONCLUSION

Our results highlight the substantial impact of NPIs, including lockdowns and curfews, in controlling the COVID-19 pandemic. We also demonstrate the value of the 100 days objective of the Coalition for Epidemic Preparedness Innovations (CEPI) initiative for vaccine availability.

Characterizing collective physical distancing in the U.S. during the first nine months of the COVID-19 pandemic

The COVID-19 pandemic offers an unprecedented natural experiment providing insights into the emergence of collective behavioral changes of both exogenous (government mandated) and endogenous (spontaneous reaction to infection risks) origin. Here, we characterize collective physical distancing-mobility reductions, minimization of contacts, shortening of contact duration-in response to the COVID-19 pandemic in the pre-vaccine era by analyzing de-identified, privacy-preserving location data for a panel of over 5.5 million anonymized, opted-in U.S. devices. We define five indicators of users' mobility and proximity to investigate how the emerging collective behavior deviates from typical pre-pandemic patterns during the first nine months of the COVID-19 pandemic. We analyze both the dramatic changes due to the government mandated mitigation policies and the more spontaneous societal adaptation into a new (physically distanced) normal in the fall 2020. Using the indicators here defined we show that: a) during the COVID-19 pandemic, collective physical distancing displayed different phases and was heterogeneous across geographies, b) metropolitan areas displayed stronger reductions in mobility and contacts than rural areas; c) stronger reductions in commuting patterns are observed in geographical areas with a higher share of teleworkable jobs; d) commuting volumes during and after the lockdown period negatively correlate with unemployment rates; and e) increases in contact indicators correlate with future values of new deaths at a lag consistent with epidemiological parameters and surveillance reporting delays. In conclusion, this study demonstrates that the framework and indicators here presented can be used to analyze large-scale social distancing phenomena, paving the way for their use in future pandemics to analyze and monitor the effects of pandemic mitigation plans at the national and international levels.

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.

Epidemic graph diagrams as analytics for epidemic control in the data-rich era

COVID-19 highlighted modeling as a cornerstone of pandemic response. But it also revealed that current models may not fully exploit the high-resolution data on disease progression, epidemic surveillance and host behavior, now available. Take the epidemic threshold, which quantifies the spreading risk throughout epidemic emergence, mitigation, and control. Its use requires oversimplifying either disease or host contact dynamics. We introduce the epidemic graph diagrams to overcome this by computing the epidemic threshold directly from arbitrarily complex data on contacts, disease and interventions. A grammar of diagram operations allows to decompose, compare, simplify models with computational efficiency, extracting theoretical understanding. We use the diagrams to explain the emergence of resistant influenza variants in the 2007-2008 season, and demonstrate that neglecting non-infectious prodromic stages of sexually transmitted infections biases the predicted epidemic risk, compromising control. The diagrams are general, and improve our capacity to respond to present and future public health challenges.

Epidemic Spreading in Metapopulation Networks Coupled With Awareness Propagation

Understanding the feedback loop that links the spatiotemporal spread of infectious diseases and human behavior is an open problem. To study this problem, we develop a multiplex framework that couples epidemic spreading across subpopulations in a metapopulation network (i.e., physical layer) with the spreading of awareness about the epidemic in a communication network (i.e., virtual layer). We explicitly study the interactions between the mobility patterns across subpopulations and the awareness propagation among individuals. We analyze the coupled dynamics using microscopic Markov chains (MMCs) equations and validate the theoretical results via Monte Carlo (MC) simulations. We find that with the spreading of awareness, reducing human mobility becomes more effective in mitigating the large-scale epidemic. We also investigate the influence of varying topological features of the physical and virtual layers and the correlation between the connectivity and local population size per subpopulation. Overall the proposed modeling framework and findings contribute to the growing literature investigating the interplay between the spatiotemporal spread of epidemics and human behavior.

The district operation centres in one of the largest local health authorities in Italy to manage COVID-19 surveillance and homecare: first implementation and results of a survey addressed to general practitioners

BACKGROUND

COVID-19 pandemic represented a shock for healthcare systems. Italy was one of the first country to deal with a huge number of patients to be diagnosed, isolated, and treated with scarce evidence-based guidelines and resources. Several organizational and structural changes were needed to face the pandemic at local level. The article aims at studying the perceived impact of the newly implemented District Operation Centres (DOCs) of Local Health Authority (LHA) Roma 1 in managing active surveillance and home care of COVID-19 patients and their close contacts in cooperation with general practitioners (GPs).

METHODS

A questionnaire, developed according to Delphi methodology, was validated by 7 experts and administered to a randomized sample of GPs and family paediatricians (FPs). All medical doctors selected received a phone interview between December 2020 and January 2021. The questionnaire investigated general characteristics of the sample, relations with DOC and its usefulness, and potential developments. A descriptive analysis was performed and inferential statistical tests were used to assess differences.

RESULTS

In April 2020 the LHA Roma 1 implemented one DOCs in each local health district. 215 medical doctors were interviewed, reaching the sample target for health districts (80% CL and 10% MOE) and the whole LHA (90% CL and 5% MOE). Several aspects in the management of COVID-19 cases and close contacts of COVID-19 cases, and of the support of DOCs to GPs/FPs were investigated. More than 55% of the GPs and FPs interviewed found the DOCs useful and more than 78% would recommend a service DOC-like to other LHAs. The medical professionals interviewed would use DOCs in the future as support in treating vulnerable patients, utilizing digital health tools, enlisting specialist doctors, establishing networks, and facilitating professional counselling by nurses.

CONCLUSIONS

This study is an attempt to evaluate an organizational change happened during COVID-19 pandemic. DOCs were created to support GPs and FPs as a link between primary healthcare and public health. Although several difficulties were disclosed, DOCs' experience can help to overcome the fragmentation of the systems and the duality between primary care and public health and make the system more resilient.

COVID-19 is linked to changes in the time-space dimension of human mobility

Socio-economic constructs and urban topology are crucial drivers of human mobility patterns. During the coronavirus disease 2019 pandemic, these patterns were reshaped in their components: the spatial dimension represented by the daily travelled distance, and the temporal dimension expressed as the synchronization time of commuting routines. Here, leveraging location-based data from de-identified mobile phone users, we observed that, during lockdowns restrictions, the decrease of spatial mobility is interwoven with the emergence of asynchronous mobility dynamics. The lifting of restriction in urban mobility allowed a faster recovery of the spatial dimension compared with the temporal one. Moreover, the recovery in mobility was different depending on urbanization levels and economic stratification. In rural and low-income areas, the spatial mobility dimension suffered a more considerable disruption when compared with urbanized and high-income areas. In contrast, the temporal dimension was more affected in urbanized and high-income areas than in rural and low-income areas.

Quantifying the heterogeneous impact of lockdown policies on different socioeconomic classes during the first COVID-19 wave in Colombia

In the absence of vaccines, the most widespread reaction to curb the COVID-19 pandemic worldwide was the implementation of lockdowns or stay-at-home policies. Despite the reported usefulness of such policies, their efficiency was highly constrained by socioeconomic factors determining their feasibility and their associated outcome in terms of mobility reduction and the subsequent limitation of social activity. Here we investigate the impact of lockdown policies on the mobility patterns of different socioeconomic classes in the three major cities of Colombia during the first wave of the COVID-19 pandemic. In global terms, we find a consistent positive correlation between the reduction in mobility levels and the socioeconomic stratum of the population in the three cities, implying that those with lower incomes were less capable of adopting the aforementioned policies. Our analysis also suggests a strong restructuring of the mobility network of lowest socioeconomic strata during COVID-19 lockdown, increasing their endogenous mixing while hampering their connections with wealthiest areas due to a sharp reduction in long-distance trips.

Modelling the effect of travel-related policies on disease control in a meta-population structure

Travel restrictions, while delaying the spread of an emerging disease from the source, could inflict substantial socioeconomic burden. Travel-related policies, such as quarantine and testing of travelers, may be considered as alternative strategies to mitigate the negative impact of travel bans. We developed a meta-population, delay-differential model to evaluate a strategy that combines testing of travelers prior to departure from the source of infection with quarantine and testing at exit from quarantine in the destination population. Our results, based on early parameter estimates of SARS-CoV-2 infection, indicate that testing travelers at exit from quarantine is more effective in delaying case importation than testing them before departure or upon arrival. We show that a 1-day quarantine with an exit test could outperform a longer, 3-day quarantine without testing in delaying the outbreak peak. Rapid, large-scale testing capacities with short turnaround times provide important means of detecting infectious cases and reducing case importation, while shortening quarantine duration for travelers at destination.

Evaluating the impact of test-trace-isolate for COVID-19 management and alternative strategies

There are many contrasting results concerning the effectiveness of Test-Trace-Isolate (TTI) strategies in mitigating SARS-CoV-2 spread. To shed light on this debate, we developed a novel static-temporal multiplex network characterizing both the regular (static) and random (temporal) contact patterns of individuals and a SARS-CoV-2 transmission model calibrated with historical COVID-19 epidemiological data. We estimated that the TTI strategy alone could not control the disease spread: assuming R0 = 2.5, the infection attack rate would be reduced by 24.5%. Increased test capacity and improved contact trace efficiency only slightly improved the effectiveness of the TTI. We thus investigated the effectiveness of the TTI strategy when coupled with reactive social distancing policies. Limiting contacts on the temporal contact layer would be insufficient to control an epidemic and contacts on both layers would need to be limited simultaneously. For example, the infection attack rate would be reduced by 68.1% when the reactive distancing policy disconnects 30% and 50% of contacts on static and temporal layers, respectively. Our findings highlight that, to reduce the overall transmission, it is important to limit contacts regardless of their types in addition to identifying infected individuals through contact tracing, given the substantial proportion of asymptomatic and pre-symptomatic SARS-CoV-2 transmission.

A national nudge study of differently framed messages to increase COVID-19 vaccine uptake in Saudi Arabia: A randomized controlled trial

Background

During the COVID-19 pandemic, Saudi Arabia witnessed hesitancy from a proportion of the population toward taking the vaccine; thus, it was necessary to nudge them to uptake it. This study was conducted to assess the impact of using different types of messages to nudge the public to increase the proportion of vaccinated individuals.

Methods

This study is a multi-arm randomized controlled trial aiming to assess the efficacy of using differently framed messages that appear as pop-notifications in Sehatty application. Of those who preregistered to receive a COVID-19 vaccine but didn't take it according to the Saudi national vaccine registry (n = 1,291,686), 12,000 individuals were randomly recruited and randomly assigned to one of five intervention groups (commitment, loss aversion, salience, social norms, and ego) or a control group. To ensure the exposure occurred in the intervention groups, we included only those who received the notification, which was confirmed by checking the information technology system. We used the Chi-square test to compare each intervention group against the control group separately. Also, we used the same test to investigate whether sex and age influenced the percentage of booked appointments in the intervention groups.

Results

Social norms, ego, salience and loss aversion groups had higher percentages of booked appointments when compared to the control group (21.0%, p = 0.001; 19.1%, p = 0.011; 19.0%, p = 0.013; 18.4%, p = 0.034, respectively). Moreover, when combining the intervention groups, the percentage was higher than the control group (p < 0.001). The percentages of booked appointments made by Young adults (18-35 years old) were higher than that of adults over 35 years old in the social norms (22.6%, p = 0.016) and ego groups (21.0%, p = 0.010). At the same time, sex didn't affect the percentages of booked appointments in any group.

Conclusion

Using different framings of messages to nudge the public to take vaccines can help increase the percentage of immunized individuals in a community. Nudges can boost the public health of a population during an unusual spread of vaccine-preventable diseases. Findings might also inspire governmental responses to other public health situations.

Epidemic Management via Imperfect Testing: A Multi-criterial Perspective

Diagnostic testing may represent a key component in response to an ongoing epidemic, especially if coupled with containment measures, such as mandatory self-isolation, aimed to prevent infectious individuals from furthering onward transmission while allowing non-infected individuals to go about their lives. However, by its own nature as an imperfect binary classifier, testing can produce false negative or false positive results. Both types of misclassification are problematic: while the former may exacerbate the spread of disease, the latter may result in unnecessary isolation mandates and socioeconomic burden. As clearly shown by the COVID-19 pandemic, achieving adequate protection for both people and society is a crucial, yet highly challenging task that needs to be addressed in managing large-scale epidemic transmission. To explore the trade-offs imposed by diagnostic testing and mandatory isolation as tools for epidemic containment, here we present an extension of the classical Susceptible-Infected-Recovered model that accounts for an additional stratification of the population based on the results of diagnostic testing. We show that, under suitable epidemiological conditions, a careful assessment of testing and isolation protocols can contribute to epidemic containment, even in the presence of false negative/positive results. Also, using a multi-criterial framework, we identify simple, yet Pareto-efficient testing and isolation scenarios that can minimize case count, isolation time, or seek a trade-off solution for these often contrasting epidemic management objectives.

Associations between depression, fear of COVID-19 infection and students' self-care measures used during the first wave of the pandemic

BACKGROUND

COVID-19 lockdown resulted in the closure of schools with associated problems. The aim of this study was to determine the associations between depression, fear of contracting COVID-19 infection and the use of self-care measures by college students during the first wave of the COVID-19 pandemic.

METHODS

This was a cross-sectional study that collected data from undergraduate and postgraduate college students 18 years and older from 152 countries between June and December 2020. Study participants were recruited through crowdsourcing using various social media platforms including Facebook, Twitter, and Instagram, WhatsApp groups and emails to participants in the collaborators' networks. The dependent variables were fear of contracting COVID-19 and depression while the independent variable was students' self-care measures. Multivariable logistic regression models were conducted to assess the associations between the dependent and independent variables.

RESULTS

Of the 2840 respondents, 1305 (46.0%) had fears of contracting COVID-19 and 599 (21.1%) reported depression. The most common self-care measures were phone calls with friends/family (60.1%) and video chat (52.8%). Learning a new skill was significantly associated with higher odds of fear of contracting COVID-19 (AOR = 1.669) and lower odds of having depression (AOR = 0.684). Talking to friends/family through video chat (AOR = 0.809) was significantly associated with lower odds of feeling depressed while spending time with pets (AOR = 1.470) and taking breaks from the news/social media (AOR = 1.242) were significantly associated with higher odds of feeling depressed. Students from lower middle-income countries (AOR = 0.330) had significantly lower odds of feeling depressed than students from low-income countries.

CONCLUSION

Self-care strategies involving social interactions were associated with less depression. Coping strategies with more cognitive demands may significantly reduce the risk of fear of COVID-19. Special attention needs to be given to students in low-income countries who have higher odds of depression during the pandemic than students from other countries.

Joint Analysis of the Epidemic Evolution and Human Mobility During the First Wave of COVID-19 in Spain: Retrospective Study

BACKGROUND

The initial wave of the COVID-19 pandemic placed a tremendous strain on health care systems worldwide. To mitigate the spread of the virus, many countries implemented stringent nonpharmaceutical interventions (NPIs), which significantly altered human behavior both before and after their enactment. Despite these efforts, a precise assessment of the impact and efficacy of these NPIs, as well as the extent of human behavioral changes, remained elusive.

OBJECTIVE

In this study, we conducted a retrospective analysis of the initial wave of COVID-19 in Spain to better comprehend the influence of NPIs and their interaction with human behavior. Such investigations are vital for devising future mitigation strategies to combat COVID-19 and enhance epidemic preparedness more broadly.

METHODS

We used a combination of national and regional retrospective analyses of pandemic incidence alongside large-scale mobility data to assess the impact and timing of government-implemented NPIs in combating COVID-19. Additionally, we compared these findings with a model-based inference of hospitalizations and fatalities. This model-based approach enabled us to construct counterfactual scenarios that gauged the consequences of delayed initiation of epidemic response measures.

RESULTS

Our analysis demonstrated that the pre-national lockdown epidemic response, encompassing regional measures and heightened individual awareness, significantly contributed to reducing the disease burden in Spain. The mobility data indicated that people adjusted their behavior in response to the regional epidemiological situation before the nationwide lockdown was implemented. Counterfactual scenarios suggested that without this early epidemic response, there would have been an estimated 45,400 (95% CI 37,400-58,000) fatalities and 182,600 (95% CI 150,400-233,800) hospitalizations compared to the reported figures of 27,800 fatalities and 107,600 hospitalizations, respectively.

CONCLUSIONS

Our findings underscore the significance of self-implemented prevention measures by the population and regional NPIs before the national lockdown in Spain. The study also emphasizes the necessity for prompt and precise data quantification prior to enacting enforced measures. This highlights the critical interplay between NPIs, epidemic progression, and human behavior. This interdependence presents a challenge in predicting the impact of NPIs before they are implemented.

Mathematical model of coffee tree's rust control using snails as biological agents

Coffee rust is one of the main diseases that affect coffee plantations worldwide, causing large-scale ecological and economic damage. While multiple methods have been proposed to tackle this challenge, using snails as biological agents have shown to be the most consistent and promising approach. However, snails are an invasive species, and overusing them can cause devastating outcomes. In this paper, we develop and explore an ecological-epidemiological mathematical model for the coffee tree rust pandemic control using snails as biological agents. We analyze the equilibria of the proposed system with their stability properties. In addition, we perform numerical analysis to obtain the sensitivity of the system to different changes and manipulation of the snails pandemic control, under specific conditions. Finally, we propose an in silico mechanism to obtain an analytical connection between the system's initial condition and the number of snails needed to optimally control the rust pandemic spread while preventing the snail population to grow unmanageably. Our model can be used to optimize the usage of snails as biological agents to control the rust pandemic in spatially-small areas, by predicting the number of snails one needs to introduce to the ecosystem in order to obtain a desired outcome.

Multiregional, multi-industry impacts of fairness on pandemic policies

The health and economic crisis caused by the COVID-19 pandemic highlights the necessity for a deeper understanding and investigation of state- and industry-level mitigation policies. While different control strategies in the early stages, such as lockdowns and school and business closures, have helped decrease the number of infections, these strategies have had an adverse economic impact on businesses and some controversial impacts on social justice. Therefore, optimal timing and scale of closure and reopening strategies are required to prevent both different waves of the pandemic and the negative socioeconomic impact of control strategies. This article proposes a novel multiobjective mixed-integer linear programming formulation, which results in the optimal timing of closure and reopening of states and industries in each. The three objectives being pursued include: (i) the epidemiological impact of the pandemic in terms of the percentage of the infected population; (ii) the social vulnerability index of the pandemic policy based on the vulnerability of communities to getting infected, and for losing their job; and (iii) the economic impact of the pandemic based on the inoperability of industries in each state. The proposed model is implemented on a dataset that includes 50 states, the District of Columbia, and 19 industries in the United States. The Pareto-optimal solutions suggest that for any control decision (state and industry closure or reopening), the economic impact and the epidemiological impact change in the opposite direction.

Epidemic modeling with heterogeneity and social diffusion

We propose and analyze a family of epidemiological models that extend the classic Susceptible-Infectious-Recovered/Removed (SIR)-like framework to account for dynamic heterogeneity in infection risk. The family of models takes the form of a system of reaction-diffusion equations given populations structured by heterogeneous susceptibility to infection. These models describe the evolution of population-level macroscopic quantities S, I, R as in the classical case coupled with a microscopic variable f, giving the distribution of individual behavior in terms of exposure to contagion in the population of susceptibles. The reaction terms represent the impact of sculpting the distribution of susceptibles by the infection process. The diffusion and drift terms that appear in a Fokker-Planck type equation represent the impact of behavior change both during and in the absence of an epidemic. We first study the mathematical foundations of this system of reaction-diffusion equations and prove a number of its properties. In particular, we show that the system will converge back to the unique equilibrium distribution after an epidemic outbreak. We then derive a simpler system by seeking self-similar solutions to the reaction-diffusion equations in the case of Gaussian profiles. Notably, these self-similar solutions lead to a system of ordinary differential equations including classic SIR-like compartments and a new feature: the average risk level in the remaining susceptible population. We show that the simplified system exhibits a rich dynamical structure during epidemics, including plateaus, shoulders, rebounds and oscillations. Finally, we offer perspectives and caveats on ways that this family of models can help interpret the non-canonical dynamics of emerging infectious diseases, including COVID-19.

Optimal timing of non-pharmaceutical interventions during an epidemic

In response to the recent outbreak of the SARS-CoV-2 virus governments have aimed to reduce the virus's spread through, inter alia, non-pharmaceutical intervention. We address the question when such measures should be implemented and, once implemented, when to remove them. These issues are viewed through a real-options lens and we develop an SIRD-like continuous-time Markov chain model to analyze a sequence of options: the option to intervene and introduce measures and, after intervention has started, the option to remove these. Measures can be imposed multiple times. We implement our model using estimates from empirical studies and, under fairly general assumptions, our main conclusions are that: (1) measures should be put in place not long after the first infections occur; (2) if the epidemic is discovered when there are many infected individuals already, then it is optimal never to introduce measures; (3) once the decision to introduce measures has been taken, these should stay in place until the number of susceptible or infected members of the population is close to zero; (4) it is never optimal to introduce a tier system to phase-in measures but it is optimal to use a tier system to phase-out measures; (5) a more infectious variant may reduce the duration of measures being in place; (6) the risk of infections being brought in by travelers should be curbed even when no other measures are in place. These results are robust to several variations of our base-case model.

Stochastic simulation of successive waves of COVID-19 in the province of Barcelona

Analytic compartmental models are currently used in mathematical epidemiology to forecast the COVID-19 pandemic evolution and explore the impact of mitigation strategies. In general, such models treat the population as a single entity, losing the social, cultural and economical specificities. We present a network model that uses socio-demographic datasets with the highest available granularity to predict the spread of COVID-19 in the province of Barcelona. The model is flexible enough to incorporate the effect of containment policies, such as lockdowns or the use of protective masks, and can be easily adapted to future epidemics. We follow a stochastic approach that combines a compartmental model with detailed individual microdata from the population census, including social determinants and age-dependent strata, and time-dependent mobility information. We show that our model reproduces the dynamical features of the disease across two waves and demonstrates its capability to become a powerful tool for simulating epidemic events.

Modeling the impact of national and regional lockdowns on the 2020 spring wave of COVID-19 in France

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Modeling the impact of national and regional lockdowns on the 2020 spring wave of COVID-19 in France

Several countries have implemented lockdowns to control their COVID-19 epidemic. However, questions like "where" and "when" still require answers. We assessed the impact of national and regional lockdowns considering the French first epidemic wave of COVID-19 as a case study. In a regional lockdown scenario aimed at preventing intensive care units (ICU) saturation, almost all French regions would have had to implement a lockdown within 10 days and 96% of ICU capacities would have been used. For slowly growing epidemics, with a lower reproduction number, the expected delays between regional lockdowns increase. However, the public health costs associated with these delays tend to grow with time. In a quickly growing pandemic wave, defining the timing of lockdowns at a regional rather than national level delays by a few days the implementation of a nationwide lockdown but leads to substantially higher morbidity, mortality, and stress on the healthcare system.

Sensing dynamic human activity zones using geo-tagged big data in Greater London, UK during the COVID-19 pandemic

Exploration of dynamic human activity gives significant insights into understanding the urban environment and can help to reinforce scientific urban management strategies. Lots of studies are arising regarding the significant human activity changes in global metropolises and regions affected by COVID-19 containment policies. However, the variations of human activity dynamics amid different phases divided by the non-pharmaceutical intervention policies (e.g., stay-at-home, lockdown) have not been investigated across urban areas in space and time and discussed with the urban characteristic determinants. In this study, we aim to explore the influence of different restriction phases on dynamic human activity through sensing human activity zones (HAZs) and their dominated urban characteristics. Herein, we proposed an explainable analysis framework to explore the HAZ variations consisting of three parts, i.e., footfall detection, HAZs delineation and the identification of relationships between urban characteristics and HAZs. In our study area of Greater London, United Kingdom, we first utilised the footfall detection method to extract human activity metrics (footfalls) counted by visits/stays at space and time from the anonymous mobile phone GPS trajectories. Then, we characterised HAZs based on the homogeneity of daily human footfalls at census output areas (OAs) during the predefined restriction phases in the UK. Lastly, we examined the feature importance of explanatory variables as the metric of the relationship between human activity and urban characteristics using machine learning classifiers. The results show that dynamic human activity exhibits statistically significant differences in terms of the HAZ distributions across restriction phases and is strongly associated with urban characteristics (e.g., specific land use types) during the COVID-19 pandemic. These findings can improve the understanding of the variation of human activity patterns during the pandemic and offer insights into city management resource allocation in urban areas concerning dynamic human activity.

Data-driven multiscale dynamical framework to control a pandemic evolution with non-pharmaceutical interventions

Before the availability of vaccines, many countries have resorted multiple times to drastic social restrictions to prevent saturation of their health care system, and to regain control over an otherwise exponentially increasing COVID-19 pandemic. With the advent of data-sharing, computational approaches are key to efficiently control a pandemic with non-pharmaceutical interventions (NPIs). Here we develop a data-driven computational framework based on a time discrete and age-stratified compartmental model to control a pandemic evolution inside and outside hospitals in a constantly changing environment with NPIs. Besides the calendrical time, we introduce a second time-scale for the infection history, which allows for non-exponential transition probabilities. We develop inference methods and feedback procedures to successively recalibrate model parameters as new data becomes available. As a showcase, we calibrate the framework to study the pandemic evolution inside and outside hospitals in France until February 2021. We combine national hospitalization statistics from governmental websites with clinical data from a single hospital to calibrate hospitalization parameters. We infer changes in social contact matrices as a function of NPIs from positive testing and new hospitalization data. We use simulations to infer hidden pandemic properties such as the fraction of infected population, the hospitalisation probability, or the infection fatality ratio. We show how reproduction numbers and herd immunity levels depend on the underlying social dynamics.

Lifting COVID-19 mitigation measures in Spain (May-June 2020)

INTRODUCTION

The state of alarm was declared in Spain due to the COVID-19 epidemic on March 14, 2020, and established population confinement measures. The objective is to describe the process of lifting these mitigation measures.

METHODS

The Plan for the Transition to a New Normality, approved on April 28, contained four sequential phases with progressive increase in socio-economic activities and population mobility. In parallel, a new strategy for early diagnosis, surveillance and control was implemented. A bilateral decision mechanism was established between the Spanish Government and the autonomous communities (AC), guided by a set of qualitative and quantitative indicators capturing the epidemiological situation and core capacities. The territorial units were established ad-hoc and could be from Basic Health Zones to entire AC.

RESULTS

The process run from May 4 to June 21, 2020. AC implemented plans for reinforcement of core capacities. Incidence decreased from a median (50% of territories) of 7.4 per 100,000 in 7 days at the beginning to 2.5 at the end. Median PCR testing increased from 53% to 89% of suspected cases and PCR total capacity from 4.5 to 9.8 per 1000 inhabitants weekly; positivity rate decreased from 3.5% to 1.8%. Median proportion of cases with traced contacts increased from 82% to 100%.

CONCLUSION

Systematic data collection, analysis, and interterritorial dialogue allowed adequate process control. The epidemiological situation improved but, mostly, the process entailed a great reinforcement of core response capacities nation-wide, under common criteria. Maintaining and further reinforcing capacities remained crucial for responding to future waves.

Minimizing Viral Transmission in COVID-19 Like Pandemics: Technologies, Challenges, and Opportunities

Coronavirus (COVID-19) pandemic has incurred huge loss to human lives throughout the world. Scientists, researchers, and doctors are trying their best to develop and distribute the COVID-19 vaccine throughout the world at the earliest. In current circumstances, different tracking systems are utilized to control or stop the spread of the virus till the whole population of the world gets vaccinated. To track and trace patients in COVID-19 like pandemics, various tracking systems based on different technologies are discussed and compared in this paper. These technologies include, cellular, cyber, satellite-based radio navigation and low range wireless technologies. The main aim of this paper is to conduct a comprehensive survey that can overview all such tracking systems, which are used in minimizing the spread of COVID-19 like pandemics. This paper also highlights the shortcoming of each tracking systems and suggests new mechanisms to overcome such limitations. In addition, the authors propose some futuristic approaches to track patients in prospective pandemics, based on artificial intelligence and big data analysis. Potential research directions, challenges, and the introduction of next-generation tracking systems for minimizing the spread of prospective pandemics, are also discussed at the end.

A real-time regional model for COVID-19: Probabilistic situational awareness and forecasting

The COVID-19 pandemic is challenging nations with devastating health and economic consequences. The spread of the disease has revealed major geographical heterogeneity because of regionally varying individual behaviour and mobility patterns, unequal meteorological conditions, diverse viral variants, and locally implemented non-pharmaceutical interventions and vaccination roll-out. To support national and regional authorities in surveilling and controlling the pandemic in real-time as it unfolds, we here develop a new regional mathematical and statistical model. The model, which has been in use in Norway during the first two years of the pandemic, is informed by real-time mobility estimates from mobile phone data and laboratory-confirmed case and hospitalisation incidence. To estimate regional and time-varying transmissibility, case detection probabilities, and missed imported cases, we developed a novel sequential Approximate Bayesian Computation method allowing inference in useful time, despite the high parametric dimension. We test our approach on Norway and find that three-week-ahead predictions are precise and well-calibrated, enabling policy-relevant situational awareness at a local scale. By comparing the reproduction numbers before and after lockdowns, we identify spatially heterogeneous patterns in their effect on the transmissibility, with a stronger effect in the most populated regions compared to the national reduction estimated to be 85% (95% CI 78%-89%). Our approach is the first regional changepoint stochastic metapopulation model capable of real time spatially refined surveillance and forecasting during emergencies.

Impact of Post-Covid-19 on driver behaviour: A perspective towards pandemic-sustained transportation

Introduction

With the announcement of novel Coronavirus disease 2019 (Covid-19) as a pandemic by World Health Organization (WHO) in March 2020, the whole world went into a lockdown that heavily affected human economic and social life. Since December 2020, with the discovery of effective vaccines, the world is now returning to some normality, particularly for those who are vaccinated. The multimodal transportation has resumed with majority of vaccinated drivers being back on road, driving to their work, and providing transport services. However, there are still several long-term Post-Covid-19 factors, affecting driver health and psychology.

Methods

The study deployed a systematic search strategy and selected 62 research publications after rigorous evaluation of the literature. The review was based on (1) forming the inclusion and exclusion criteria, (2) selecting the appropriate keywords, and (3) searching of relevant publications and assessing the eligible articles.

Results

A broad perspective study is carried out to gauge the impact of Post-Covid-19 scenarios on the driver physical health and mindset in the context of road safety and pandemic-sustained transportation. It was found that the Post-Covid-19 factors such as wearing face-mask during driving, taking oral anti-viral drugs, and fear of contracting disease, significantly impact the driver's performance and situation awareness skills. The analysis suggested that driver's health vitals and psychological driving awareness can be precisely detected through hybrid driver state monitoring methods.

Conclusions

The paper conducts a comprehensive review of the published work and provides unique research opportunities to counteract the challenges involved in precise monitoring of driver behaviour under the effects of different Post-Covid-19 factors. The perspective suggested the possible solutions to live with the pandemic in the context of pandemic-sustained transportation.

Clinical presentation of acute primary angle closure during the COVID-19 epidemic lockdown

Purpose

This study aimed to investigate the clinical presentation of acute primary angle closure (APAC) during the COVID-19 epidemic lockdown in Wuhan.

Methods

Consecutive patients seeking APAC treatment at the Wuhan Aier Eye Hospital during the 76 days (January 23-April 8, 2020) when the lockdown policy was implemented due to the COVID-19 pandemic were compared to those during the same period the following year (January 23-April 8, 2021), when the lockdown policy was not implemented. The cohorts were compared to assess demographic variables and clinical presentations.

Results

A total of 54 patients (64 eyes) were included in the 2020, compared with 46 patients (51 eyes) in the 2021. Demographic factors were similar between the groups. Significantly more patients developed blindness in the 2020 cohort (21.87%) than in the 2021 cohort (7.84%). Patients in the 2020 showed a longer time from symptom to treatment (241.84 ± 211.95 h in 2020 vs. 121.53 ± 96.12 h in 2021; P = 0.001), higher intraocular pressure at presentation (52.63 ± 12.45 mmHg in 2020 vs. 45.16 ± 9.79 mmHg in 2021; P = 0.001), larger pupil diameter (5.47 ± 1.62 mm in 2020 vs. 4.33 ± 1.27 mm in 2021; P = 0.001), and more glaucomatous optic neuropathy diagnoses [20/64 eyes (31.25%) in 2020 vs. 7/51 eyes (13.73%) in 2021; P = 0.03].

Conclusion

The time between the onset of APAC symptoms and its treatment during the COVID-19 epidemic lockdown was significantly prolonged, which increased the blindness rate of APAC patients.

Designing an optimal sequence of non-pharmaceutical interventions for controlling COVID-19

The COVID-19 pandemic has had an unprecedented impact on global health and the economy since its inception in December, 2019 in Wuhan, China. Non-pharmaceutical interventions (NPI) like lockdowns and curfews have been deployed by affected countries for controlling the spread of infections. In this paper, we develop a Mixed Integer Non-Linear Programming (MINLP) epidemic model for computing the optimal sequence of NPIs over a planning horizon, considering shortages in doctors and hospital beds, under three different lockdown scenarios. We analyse two strategies - centralised (homogeneous decisions at the national level) and decentralised (decisions differentiated across regions), for two objectives separately - minimization of infections and deaths, using actual pandemic data of France. We linearize the quadratic constraints and objective functions in the MINLP model and convert it to a Mixed Integer Linear Programming (MILP) model. A major result that we show analytically is that under the epidemic model used, the optimal sequence of NPIs always follows a decreasing severity pattern. Using this property, we further simplify the MILP model into an Integer Linear Programming (ILP) model, reducing computational time up to 99%. Our numerical results show that a decentralised strategy is more effective in controlling infections for a given severity budget, yielding up to 20% lesser infections, 15% lesser deaths and 60% lesser shortages in healthcare resources. These results hold without considering logistics aspects and for a given level of compliance of the population.

Influenza vaccination coverage among health-care workers during the COVID-19 epidemic in 2020/2021 influenza season: Evidence from a web-based survey in northwestern China

Vaccinating health-care workers against influenza during the COVID-19 pandemic can effectively prevent and control influenza and reduce COVID-19 strain on health systems. This study was conducted to explore influenza vaccination coverage and determinants among health-care workers during the COVID-19 pandemic in 2020/2021 influenza season in Ningxia. This cross-sectional survey included demographic characteristics of health-care workers, influenza vaccination status, reasons for not getting vaccinated, and whether influenza vaccination was recommended for others. We found that influenza vaccine rate of health-care workers was 39.6%. A binary logistic regression analysis showed that health-care workers' vaccination coverage was higher when the individuals were aware of the effect of the influenza vaccine (OR = 0.624, 95% CI: 0.486-0.802). Health-care workers who from internal medicine (OR = 1.494, 95% CI: 1.146-1.948), pediatrics (OR = 2.091, 95% CI: 1.476-2.962), and surgery departments (OR = 1.373, 95% CI: 1.014-1.859) had a lower coverage than those who worked in vaccination and infectious disease departments. The main reasons that some stated for not getting vaccinated were that they felt it was unnecessary (52.22%). Health-care workers who were vaccinated against influenza were more likely to recommend influenza vaccination to their patients than health-care workers who had not been vaccinated. The incidence of influenza among health-care workers was higher than that of the general population in Ningxia. Under the policy of voluntary and self-pay influenza vaccination in Ningxia, the coverage rate of influenza vaccine among health-care workers was far below the vaccination requirements of influenza vaccine in influenza season even during the COVID-19 epidemic.

Predicted Impacts of Booster, Immunity Decline, Vaccination Strategies, and Non-Pharmaceutical Interventions on COVID-19 Outcomes in France

The major economic and health consequences of COVID-19 called for various protective measures and mass vaccination campaigns. A previsional model was used to predict the future impacts of various measure combinations on COVID-19 mortality over a 400-day period in France. Calibrated on previous national hospitalization and mortality data, an agent-based epidemiological model was used to predict individual and combined effects of booster doses, vaccination of refractory adults, and vaccination of children, according to infection severity, immunity waning, and graded non-pharmaceutical interventions (NPIs). Assuming a 1.5 hospitalization hazard ratio and rapid immunity waning, booster doses would reduce COVID-19-related deaths by 50-70% with intensive NPIs and 93% with moderate NPIs. Vaccination of initially-refractory adults or children ≥5 years would half the number of deaths whatever the infection severity or degree of immunity waning. Assuming a 1.5 hospitalization hazard ratio, rapid immunity waning, moderate NPIs and booster doses, vaccinating children ≥12 years, ≥5 years, and ≥6 months would result in 6212, 3084, and 3018 deaths, respectively (vs. 87,552, 64,002, and 48,954 deaths without booster, respectively). In the same conditions, deaths would be 2696 if all adults and children ≥12 years were vaccinated and 2606 if all adults and children ≥6 months were vaccinated (vs. 11,404 and 3624 without booster, respectively). The model dealt successfully with single measures or complex combinations. It can help choosing them according to future epidemic features, vaccination extensions, and population immune status.

A Comparison Study of the Detection Limit of Omicron SARS-CoV-2 Nucleocapsid by Various Rapid Antigen Tests

Rapid antigen tests (RATs) are widely used worldwide to detect SARS-CoV-2 since they are an easy-to-use kit and offer rapid results. The RAT detects the presence of the nucleocapsid protein, which is located inside the virus. However, the sensitivity of the different RATs varies between commercially available kits. The test result might change due to various factors, such as the variant type, infection date, swab's surface, the manner in which one performs the testing and the mucus components. Here, we compare the detection limit of seven commercially available RATs by introducing them to known SARS-CoV-2 nucleocapsid protein amounts from the Omicron variant. It allows us to determine the detection limit, disregarding the influences of other factors. A lower detection limit of the RAT is necessary since earlier detection will help reduce the spread of the virus and allow faster treatment, which might be crucial for the population at risk.

Has the COVID-19 pandemic converged across countries?

The outbreak of COVID-19 has induced economic and financial disruptions to global economies, consistent with those experienced during previous episodes of economic or financial crises. This study offers a critical perspective into the spread of the virus by investigating the convergence patterns of COVID-19 across 155 countries from March 2020 to August 2021. The club clustering algorithm is used to verify the convergence patterns of infection and death rates in these countries. The findings show that full panel convergence cannot be achieved indicating the presence of sub-convergent clusters. Cluster formation for death rates includes the Americas, Africa, the Middle East, and Asia, among others. To understand the factors driving these results, we analyse the determinants of the convergence process of COVID-19. The probability of belonging to a cluster with higher death intensity increases with being above the age of 65, poverty, and for female smokers while handwashing shows beneficial effect on case intensity.

Data-driven approach in a compartmental epidemic model to assess undocumented infections

Nowcasting and forecasting of epidemic spreading rely on incidence series of reported cases to derive the fundamental epidemiological parameters for a given pathogen. Two relevant drawbacks for predictions are the unknown fractions of undocumented cases and levels of nonpharmacological interventions, which span highly heterogeneously across different places and times. We describe a simple data-driven approach using a compartmental model including asymptomatic and pre-symptomatic contagions that allows to estimate both the level of undocumented infections and the value of effective reproductive number R t from time series of reported cases, deaths, and epidemiological parameters. The method was applied to epidemic series for COVID-19 across different municipalities in Brazil allowing to estimate the heterogeneity level of under-reporting across different places. The reproductive number derived within the current framework is little sensitive to both diagnosis and infection rates during the asymptomatic states. The methods described here can be extended to more general cases if data is available and adapted to other epidemiological approaches and surveillance data.

From individual-based epidemic models to McKendrick-von Foerster PDEs: a guide to modeling and inferring COVID-19 dynamics

We present a unifying, tractable approach for studying the spread of viruses causing complex diseases requiring to be modeled using a large number of types (e.g., infective stage, clinical state, risk factor class). We show that recording each infected individual's infection age, i.e., the time elapsed since infection, has three benefits. First, regardless of the number of types, the age distribution of the population can be described by means of a first-order, one-dimensional partial differential equation (PDE) known as the McKendrick-von Foerster equation. The frequency of type i is simply obtained by integrating the probability of being in state i at a given age against the age distribution. This representation induces a simple methodology based on the additional assumption of Poisson sampling to infer and forecast the epidemic. We illustrate this technique using French data from the COVID-19 epidemic. Second, our approach generalizes and simplifies standard compartmental models using high-dimensional systems of ordinary differential equations (ODEs) to account for disease complexity. We show that such models can always be rewritten in our framework, thus, providing a low-dimensional yet equivalent representation of these complex models. Third, beyond the simplicity of the approach, we show that our population model naturally appears as a universal scaling limit of a large class of fully stochastic individual-based epidemic models, where the initial condition of the PDE emerges as the limiting age structure of an exponentially growing population starting from a single individual.

Activity-driven network modeling and control of the spread of two concurrent epidemic strains

The emergency generated by the current COVID-19 pandemic has claimed millions of lives worldwide. There have been multiple waves across the globe that emerged as a result of new variants, due to arising from unavoidable mutations. The existing network toolbox to study epidemic spreading cannot be readily adapted to the study of multiple, coexisting strains. In this context, particularly lacking are models that could elucidate re-infection with the same strain or a different strain-phenomena that we are seeing experiencing more and more with COVID-19. Here, we establish a novel mathematical model to study the simultaneous spreading of two strains over a class of temporal networks. We build on the classical susceptible-exposed-infectious-removed model, by incorporating additional states that account for infections and re-infections with multiple strains. The temporal network is based on the activity-driven network paradigm, which has emerged as a model of choice to study dynamic processes that unfold at a time scale comparable to the network evolution. We draw analytical insight from the dynamics of the stochastic network systems through a mean-field approach, which allows for characterizing the onset of different behavioral phenotypes (non-epidemic, epidemic, and endemic). To demonstrate the practical use of the model, we examine an intermittent stay-at-home containment strategy, in which a fraction of the population is randomly required to isolate for a fixed period of time.

A cross-country comparison of Covid-19 containment measures and their effects on the epidemic curves

BACKGROUND

European countries are still searching to eliminate or contain the Covid-19 pandemic. A variety of approaches have achieved different levels of success in limiting the spread of the disease early and preventing avoidable deaths. Governmental policy responses may explain these differences and this study aims to describe evidence about the effectiveness of containment measures throughout the course of the pandemic in five European countries (France, Germany, Italy, Spain and the UK).

METHODS

The research approach adopted consisted of three steps: 1) Build a Containment Index (C.I.) that considers nine parameters to make an assessment on the strength of measures; 2) Develop dynamic epidemiological models for forecasting purposes; 3) Predict case numbers by assuming containment measures remain constant for a period of 30 days.

RESULTS

Our analysis revealed that in the five European countries we compared, the use of different approaches definitively affected the effectiveness of containment measures for the Covid-19 pandemic.

CONCLUSION

The evidence found in our research can be useful to inform policy makers' decisions when deciding to introduce or relax containment measures and their timing, both during the current pandemic or in addressing possible future health crises.

The Impact of the COVID-19 Pandemic on the Dental Emergency Service from Oradea, Romania: A Retrospective Study

The COVID-19 pandemic affected the daily lives of the global population, not only in terms of social interaction but also in terms of access to medical and dental care. Non-urgent dental treatments could not be continued during the lockdown and only a small number of dental centres addressed patients with dental emergencies. The aim of this study was to evaluate the socio-demographic characteristics (age, gender, and living environment) of the individuals that accessed the dental emergency centre in Oradea (North-West Romania) and the main causes for accessing the dental emergency service among the population of Oradea (North-West Romania), during the COVID-19 lockdown, between March and May 2020 and, furthermore, to compare the results obtained in the lockdown timeframe (March–May 2020), with the results obtained in the corresponding timeframe in the pre-lockdown year (March–May 2019) and post-lockdown year (March–May 2021). The retrospective study was carried out by analysing the medical records of the patients who were treated in the dental emergency service of the Oradea County Emergency Clinical Hospital in the following periods: March–May 2019, March–May 2020, and March–May 2021. Most patients were treated in 2020, during the lockdown (n = 784), predominantly in April (n = 308). Most patients treated in April 2020 were male patients (43.7%, n = 205) and were aged between 30 and 39 years (19.4%, n = 74). The most frequent types of dental emergencies were acute apical periodontitis and acute pulpitis in all the months and years investigated. During the lockdown months of 2020, acute pulpitis was the most frequent type of emergency in March (42.2%, n = 100) and May (45.6%, n = 109), while in April, acute apical periodontitis was the most frequent type of emergency (43.5%, n = 166). The COVID-19 lockdown led to an increase in the number of patients that required emergency treatments and impacted all groups of people investigated.

A bifurcation of the peak: new patterns of traffic peaking during the COVID-19 era

This paper analyzes the emergence of two well-defined peaks during the morning peak period in the traffic flow diurnal curve. It selects six California cities as research targets, and uses California employment and household travel survey data to explain how and why this phenomenon has risen during the pandemic. The final result explains that the double-humped phenomenon results from the change in the composition of commuters during the morning peak period after the outbreak.