Non-pharmaceutical Intervention Measures Research Articles

A high-frequency mobility big-data reveals how COVID-19 spread across professions, locations and age groups.

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence, especially for China where most population has not been infected and most Omicron transmissions are silent. This paper aims to reveal the complete silent transmission dynamics of COVID-19 by agent-based simulations overlaying a big data of more than 0.7 million real individual mobility tracks without any intervention measures throughout a week in a Chinese city, with an extent of completeness and realism not attained in existing studies. Together with the empirically inferred transmission rate of COVID-19, we find surprisingly that with only 70 citizens to be infected initially, 0.33 million becomes infected silently at last. We also reveal a characteristic daily periodic pattern of the transmission dynamics, with peaks in mornings and afternoons. In addition, by inferring individual professions, visited locations and age group, we found that retailing, catering and hotel staff are more likely to get infected than other professions, and elderly and retirees are more likely to get infected at home than outside home.

Reducing societal impacts of SARS-CoV-2 interventions through subnational implementation.

To curb the initial spread of SARS-CoV-2, many countries relied on nation-wide implementation of non-pharmaceutical intervention measures, resulting in substantial socio-economic impacts. Potentially, subnational implementations might have had less of a societal impact, but comparable epidemiological impact. Here, using the first COVID-19 wave in the Netherlands as a case in point, we address this issue by developing a high-resolution analysis framework that uses a demographically stratified population and a spatially explicit, dynamic, individual contact-pattern based epidemiology, calibrated to hospital admissions data and mobility trends extracted from mobile phone signals and Google. We demonstrate how a subnational approach could achieve similar level of epidemiological control in terms of hospital admissions, while some parts of the country could stay open for a longer period. Our framework is exportable to other countries and settings, and may be used to develop policies on subnational approach as a better strategic choice for controlling future epidemics.

Alternative SIAR models for infectious diseases and applications in the study of non-compliance

In this paper, we use modified versions of the SIAR model for epidemics to propose two ways of understanding and quantifying the effect of non-compliance to non-pharmaceutical intervention measures on the spread of an infectious disease. The SIAR model distinguishes between symptomatic infected (I) and asymptomatic infected (A) populations. One modification, which is simpler, assumes a known proportion of the population does not comply with government mandates such as quarantining and social-distancing. In a more sophisticated approach, the modified model treats non-compliant behavior as a social contagion. We theoretically explore different scenarios such as the occurrence of multiple waves of infections. Local and asymptotic analyses for both models are also provided.

Collateral Impact of COVID-19 Prevention Measures on Re-Emergence of Scarlet Fever and Pertussis in Mainland China and Hong Kong China.

The incidence of scarlet fever and pertussis has increased significantly in China in recent years. During the COVID-19 pandemic, stringent non-pharmaceutical intervention measures were widely adopted to contain the spread of the virus, which may also have essential collateral impacts on other infectious diseases, such as scarlet fever and pertussis. We compared the incidence data of scarlet fever and pertussis in Mainland China and Hong Kong from 2004 to 2021 before and after the COVID-19 pandemic. The results show that the incidence of both diseases decreased significantly in 2020–2021 compared to the after-re-emergence stage in these two locations. Specifically, in 2020, scarlet fever decreased by 73.13% and pertussis by 76.63% in Mainland China, and 83.70% and 76.10%, respectively, in Hong Kong. In the absence of COVID-19, the predicted incidence of both diseases was much higher than the actual incidence in Mainland China and Hong Kong in 2020–2021. This study demonstrates that non-pharmaceutical measures implemented during the COVID-19 pandemic can partially reduce scarlet fever and pertussis re-emergence in Mainland China and Hong Kong.

Optimal health and economic impact of non-pharmaceutical intervention measures prior and post vaccination in England: a mathematical modelling study.

Background. Even with good progress on vaccination, SARS-CoV-2 infections in the UK may continue to impose a high burden of disease and therefore pose substantial challenges for health policy decision makers. Stringent government-mandated physical distancing measures (lockdown) have been demonstrated to be epidemiologically effective, but can have both positive and negative economic consequences. The duration and frequency of any intervention policy could, in theory, be optimized to maximize economic benefits while achieving substantial reductions in disease. Methods. Here, we use a pre-existing SARS-CoV-2 transmission model to assess the health and economic implications of different strengths of control through time in order to identify optimal approaches to non-pharmaceutical intervention stringency in the UK, considering the role of vaccination in reducing the need for future physical distancing measures. The model is calibrated to the COVID-19 epidemic in England and we carry out retrospective analysis of the optimal timing of precautionary breaks in 2020 and the optimal relaxation policy from the January 2021 lockdown, considering the willingness to pay (WTP) for health improvement. Results. We find that the precise timing and intensity of interventions is highly dependent upon the objective of control. As intervention measures are relaxed, we predict a resurgence in cases, but the optimal intervention policy can be established dependent upon the WTP per quality adjusted life year loss avoided. Our results show that establishing an optimal level of control can result in a reduction in net monetary loss of billions of pounds, dependent upon the precise WTP value. Conclusion. It is vital, as the UK emerges from lockdown, but continues to face an on-going pandemic, to accurately establish the overall health and economic costs when making policy decisions. We demonstrate how some of these can be quantified, employing mechanistic infectious disease transmission models to establish optimal levels of control for the ongoing COVID-19 pandemic.

Modeling the initial phase of COVID-19 epidemic: The role of age and disease severity in the Basque Country, Spain.

Declared a pandemic by the World Health Organization (WHO), COVID-19 has spread rapidly around the globe. With eventually substantial global underestimation of infection, by the end of March 2022, more than 470 million cases were confirmed, counting more than 6.1 million deaths worldwide. COVID-19 symptoms range from mild (or no) symptoms to severe illness, with disease severity and death occurring according to a hierarchy of risks, with age and pre-existing health conditions enhancing risks of disease severity. In order to understand the dynamics of disease severity during the initial phase of the pandemic, we propose a modeling framework stratifying the studied population into two groups, older and younger, assuming different risks for severe disease manifestation. The deterministic and the stochastic models are parametrized using epidemiological data for the Basque Country population referring to confirmed cases, hospitalizations and deaths, from February to the end of March 2020. Using similar parameter values, both models were able to describe well the existing data. A detailed sensitivity analysis was performed to identify the key parameters influencing the transmission dynamics of COVID-19 in the population. We observed that the population younger than 60 years old of age would contribute more to the overall force of infection than the older population, as opposed to the already existing age-structured models, opening new ways to understand the effect of population age on disease severity during the COVID-19 pandemic. With mild/asymptomatic cases significantly influencing the disease spreading and control, our findings support the vaccination strategy prioritising the most vulnerable individuals to reduce hospitalization and deaths, as well as the non-pharmaceutical intervention measures to reduce disease transmission.

Global and Local Upsurge in Monkey pox Epidemic: Current Trends, Challenges and Future Prospects

Amidst global relaxation of the Coronavirus Disease 2019 (COVID-19) intervention measures and celebration of the unprecedented grounds conquered in the fight against the pandemic, the monkeypox virus (MPXV) has emerged and remerged in many countries taking over major headlines around the globe at the present. Since 1 January and as of 22 June 2022, 50 nations and territories in five WHO Regions (Africa, America, Eastern Mediterranean, Europe, and Western Pacific) have reported 3413 laboratory-confirmed cases; along with one fatality. The Nigeria Centre for Disease Control (NCDC) received notice of a possible monkeypox epidemic in Bayelsa State on September 22, 2017. Thereafter, from Bayelsa to Rivers, Ekiti, Akwa Ibom, Lagos, Ogun and Cross River, the scourge of Monkeypox has continue to escalate five (5) years after the disease was first reported in Nigeria. An aggregate of 716 suspected cases have been recorded from 25 states nationwide between September 2017 and June 26, 2022. There have been 288 (40.2 percent) confirmed cases from 25 states out of the 716 suspected cases. So far, nine (9) deaths in total (CFR=3.1%) have been reported in 6 states between September 2017 and June 2022. At the moment, the following challenges are preventing the country from containing the virus effectively: 1) Low patient use of healthcare facilities because of stigma, cost, and low fatality rate; 2) Underreporting of cases due to inadequate access to healthcare facilities; 3) Low sample collection rate in reporting states; 4) Poor infectious disease laboratories support in remote areas; 5) Corruption and lack of political will by current administration to fight the virus; 6) Weak monkeypox monitoring, possibly related to a limited health workforce and the COVID 19 pandemic response; 7) A lack of international cooperation, unity, and coordination in the pursuit of establishing global health equity; and 8) Lack of access to authorized and safe treatment and vaccine among other things. Despite these challenges, a monkeypox-free Nigeria is possible. To this end, a safe and effective immunization of the public, as with smallpox, polio and yellow fever, is the key to the future. Until, a safe and effective vaccine becomes readily available, control and prevention lies largely on non-pharmaceutical intervention measures.

Assessment of the Effectiveness of Omicron Transmission Mitigation Strategies for European Universities Using an Agent-Based Network Model.

Returning universities to full on-campus operations while the coronavirus disease 2019 pandemic is ongoing has been a controversial discussion in many countries. The risk of large outbreaks in dense course settings is contrasted by the benefits of in-person teaching. Transmission risk depends on a range of parameters, such as vaccination coverage and efficacy, number of contacts, and adoption of nonpharmaceutical intervention measures. Owing to the generalized academic freedom in Europe, many universities are asked to autonomously decide on and implement intervention measures and regulate on-campus operations. In the context of rapidly changing vaccination coverage and parameters of the virus, universities often lack sufficient scientific insight on which to base these decisions. To address this problem, we analyzed a calibrated, data-driven agent-based simulation of transmission dynamics among 13 284 students and 1482 faculty members in a medium-sized European university. Wed use a colocation network reconstructed from student enrollment data and calibrate transmission risk based on outbreak size distributions in education institutions. We focused on actionable interventions that are part of the already existing decision process of universities to provide guidance for concrete policy decisions. Here we show that, with the Omicron variant of the severe acute respiratory syndrome coronavirus 2, even a reduction to 25% occupancy and universal mask mandates are not enough to prevent large outbreaks, given the vaccination coverage of about 85% reported for students in Austria. Our results show that controlling the spread of the virus with available vaccines in combination with nonpharmaceutical intervention measures is not feasible in the university setting if presence of students and faculty on campus is required.

Infection fatality rate and infection attack rate of COVID-19 in South American countries

BackgroundThe ongoing COVID-19 pandemic hit South America badly with multiple waves. Different COVID-19 variants have been storming across the region, leading to more severe infections and deaths even in places with high vaccination coverage. This study aims to assess the spatiotemporal variability of the COVID-19 pandemic and estimate the infection fatality rate (IFR), infection attack rate (IAR) and reproduction number ({R}_{0}) for twelve most affected South American countries.MethodsWe fit a susceptible-exposed-infectious-recovered (SEIR)-based model with a time-varying transmission rate to the reported COVID-19 deaths for the twelve South American countries with the highest mortalities. Most of the epidemiological datasets analysed in this work are retrieved from the disease surveillance systems by the World Health Organization, Johns Hopkins Coronavirus Resource Center and Our World in Data. We investigate the COVID-19 mortalities in these countries, which could represent the situation for the overall South American region. We employ COVID-19 dynamic model with-and-without vaccination considering time-varying flexible transmission rate to estimate IFR, IAR and {R}_{0} of COVID-19 for the South American countries.ResultsWe simulate the model in each scenario under suitable parameter settings and yield biologically reasonable estimates for IFR (varies between 0.303% and 0.723%), IAR (varies between 0.03 and 0.784) and {R}_{0} (varies between 0.7 and 2.5) for the 12 South American countries. We observe that the severity, dynamical patterns of deaths and time-varying transmission rates among the countries are highly heterogeneous. Further analysis of the model with the effect of vaccination highlights that increasing the vaccination rate could help suppress the pandemic in South America.ConclusionsThis study reveals possible reasons for the two waves of COVID-19 outbreaks in South America. We observed reductions in the transmission rate corresponding to each wave plausibly due to improvement in nonpharmaceutical interventions measures and human protective behavioral reaction to recent deaths. Thus, strategies coupling social distancing and vaccination could substantially suppress the mortality rate of COVID-19 in South America.Graphical

A spread model of COVID-19 with some strict anti-epidemic measures.

In the absence of specific drugs and vaccines, the best way to control the spread of COVID-19 is to adopt and diligently implement effective and strict anti-epidemic measures. In this paper, a mathematical spread model is proposed based on strict epidemic prevention measures and the known spreading characteristics of COVID-19. The equilibria (disease-free equilibrium and endemic equilibrium) and the basic regenerative number of the model are analyzed. In particular, we prove the asymptotic stability of the equilibria, including locally and globally asymptotic stability. In order to validate the effectiveness of this model, it is used to simulate the spread of COVID-19 in Hubei Province of China for a period of time. The model parameters are estimated by the real data related to COVID-19 in Hubei. To further verify the model effectiveness, it is employed to simulate the spread of COVID-19 in Hunan Province of China. The mean relative error serves to measure the effect of fitting and simulations. Simulation results show that the model can accurately describe the spread dynamics of COVID-19. Sensitivity analysis of the parameters is also done to provide the basis for formulating prevention and control measures. According to the sensitivity analysis and corresponding simulations, it is found that the most effective non-pharmaceutical intervention measures for controlling COVID-19 are to reduce the contact rate of the population and increase the quarantine rate of infected individuals.

SARS-CoV-2 variants, immune escape, and countermeasures

Coronavirus disease 2019 (COVID-19) has become a global pandemic disease. SARS-CoV-2 variants have aroused great concern and are expected to continue spreading. Although many countries have promoted roll-out vaccination, the immune barrier has not yet been fully established, indicating that populations remain susceptible to infection. In this review, we summarize the literature on variants of concern and focus on the changes in their transmissibility, pathogenicity, and resistance to the immunity constructed by current vaccines. Furthermore, we analyzed relationships between variants and breakthrough infections, as well as the paradigm of new variants in countries with high vaccination rates. Terminating transmission, continuing to strengthen variant surveillance, and combining nonpharmaceutical intervention measures and vaccines are necessary to control these variants.

Change in Pediatric Health Care Spending and Drug Utilization during the COVID-19 Pandemic.

Objective: To evaluate how the restrictive measures implemented during the SARS-CoV-2 pandemic have influenced the incidence of the most common children’s diseases and the consumption of medications in 2020 compared to 2019. Methods: We involved all family pediatricians of the local health authority of Latina, from which we requested data of monthly visits in 2019 and 2020 for six common diseases disseminated through droplets and contact, and the territorial and integrative pharmaceutical unit of the area, from which we requested data of the net expenditure regarding the most commonly used drugs at pediatric age. Results: There was significant reduction in the incidence of the evaluated diseases and in the consumption of investigated drugs between 2019 and 2020 in the months when the restrictive measures were in place, with an attenuation of this effect during the months of the gradual loosening of those measures. Conclusion: Nonpharmaceutical intervention measures have caused changes in the diffusion of common pediatric diseases. We believe that the implementation of a reasonable containment strategy, even outside of the pandemic, could positively influence the epidemiology of infectious and allergic diseases in children, and healthcare system spending.

Association of lockdowns with the protective role of ultraviolet-B (UVB) radiation in reducing COVID-19 deaths

Nations are imposing unprecedented measures at a large scale to contain the spread of the COVID-19 pandemic. While recent studies show that non-pharmaceutical intervention measures such as lockdowns may have mitigated the spread of COVID-19, those measures also lead to substantial economic and social costs, and might limit exposure to ultraviolet-B radiation (UVB). Emerging observational evidence indicates the protective role of UVB and vitamin D in reducing the severity and mortality of COVID-19 deaths. This observational study empirically outlines the protective roles of lockdown and UVB exposure as measured by the ultraviolet index (UVI). Specifically, we examine whether the severity of lockdown is associated with a reduction in the protective role of UVB exposure. We use a log-linear fixed-effects model on a panel dataset of secondary data of 155 countries from 22 January 2020 until 7 October 2020 (n = 29,327). We use the cumulative number of COVID-19 deaths as the dependent variable and isolate the mitigating influence of lockdown severity on the association between UVI and growth rates of COVID-19 deaths from time-constant country-specific and time-varying country-specific potentially confounding factors. After controlling for time-constant and time-varying factors, we find that a unit increase in UVI and lockdown severity are independently associated with − 0.85 percentage points (p.p) and − 4.7 p.p decline in COVID-19 deaths growth rate, indicating their respective protective roles. The change of UVI over time is typically large (e.g., on average, UVI in New York City increases up to 6 units between January until June), indicating that the protective role of UVI might be substantial. However, the widely utilized and least severe lockdown (governmental recommendation to not leave the house) is associated with the mitigation of the protective role of UVI by 81% (0.76 p.p), which indicates a downside risk associated with its widespread use. We find that lockdown severity and UVI are independently associated with a slowdown in the daily growth rates of cumulative COVID-19 deaths. However, we find evidence that an increase in lockdown severity is associated with significant mitigation in the protective role of UVI in reducing COVID-19 deaths. Our results suggest that lockdowns in conjunction with adequate exposure to UVB radiation might have even reduced the number of COVID-19 deaths more strongly than lockdowns alone. For example, we estimate that there would be 11% fewer deaths on average with sufficient UVB exposure during the period people were recommended not to leave their house. Therefore, our study outlines the importance of considering UVB exposure, especially while implementing lockdowns, and could inspire further clinical studies that may support policy decision-making in countries imposing such measures.

High anti-SARS-CoV-2 antibody seroconversion rates before the second wave in Manaus, Brazil, and the protective effect of social behaviour measures: results from the prospective DETECTCoV-19 cohort

SummaryBackgroundThe city of Manaus, Brazil, has seen two collapses of the health system due to the COVID-19 pandemic. We report anti-SARS-CoV-2 nucleocapsid IgG antibody seroconversion rates and associated risk factors in Manaus residents before the second wave of the epidemic in Brazil.MethodsA convenience sample of adult (aged ≥18 years) residents of Manaus was recruited through online and university website advertising into the DETECTCoV-19 study cohort. The current analysis of seroconversion included a subgroup of DETECTCoV-19 participants who had at least two serum sample collections separated by at least 4 weeks between Aug 19 and Oct 2, 2020 (visit 1), and Oct 19 and Nov 27, 2020 (visit 2). Those who reported (or had no data on) having a COVID-19 diagnosis before visit 1, and who were positive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at visit 1 were excluded. Using an in-house ELISA, the reactivity index (RI; calculated as the optical density ratio of the sample to the negative control) for serum anti-SARS-CoV-2 nucleocapsid IgG antibodies was measured at both visits. We calculated the incidence of seroconversion (defined as RI values ≤1·5 at visit 1 and ≥1·5 at visit 2, and a ratio >2 between the visit 2 and visit 1 RI values) during the study period, as well as incidence rate ratios (IRRs) through cluster-corrected and adjusted Poisson regression models to analyse associations between seroconversion and variables related to sociodemographic characteristics, health access, comorbidities, COVID-19 exposure, protective behaviours, and symptoms.Findings2496 DETECTCoV-19 cohort participants returned for a follow-up visit between Oct 19 and Nov 27, 2020, of whom 204 reported having COVID-19 before the first visit and 24 had no data regarding previous disease status. 559 participants were seropositive for anti-SARS-CoV-2 nucleocapsid IgG antibodies at baseline. Of the remaining 1709 participants who were seronegative at baseline, 71 did not meet the criteria for seroconversion and were excluded from the analyses. Among the remaining 1638 participants who were seronegative at baseline, 214 showed seroconversion at visit 2. The seroconversion incidence was 13·06% (95% CI 11·52–14·79) overall and 6·78% (5·61–8·10) for symptomatic seroconversion, over a median follow-up period of 57 days (IQR 54–61). 48·1% of seroconversion events were estimated to be asymptomatic. The sample had higher proportions of affluent and higher-educated people than those reported for the Manaus city population. In the fully adjusted and corrected model, risk factors for seroconversion before visit 2 were having a COVID-19 case in the household (IRR 1·49 [95% CI 1·21–1·83]), not wearing a mask during contact with a person with COVID-19 (1·25 [1·09–1·45]), relaxation of physical distancing (1·31 [1·05–1·64]), and having flu-like symptoms (1·79 [1·23–2·59]) or a COVID-19 diagnosis (3·57 [2·27–5·63]) between the first and second visits, whereas working remotely was associated with lower incidence (0·74 [0·56–0·97]).InterpretationAn intense infection transmission period preceded the second wave of COVID-19 in Manaus. Several modifiable behaviours increased the risk of seroconversion, including non-compliance with non-pharmaceutical interventions measures such as not wearing a mask during contact, relaxation of protective measures, and non-remote working. Increased testing in high-transmission areas is needed to provide timely information about ongoing transmission and aid appropriate implementation of transmission mitigation measures.FundingMinistry of Education, Brazil; Fundação de Amparo à Pesquisa do Estado do Amazonas; Pan American Health Organization (PAHO)/WHO.

Intense SARS-CoV-2 transmission among affluent Manaus residents preceded the second wave of the epidemic in Brazil

Intense SARS-CoV-2 transmission among affluent Manaus residents preceded the second wave of the epidemic in Brazil

Rates of SARS-CoV-2 transmission and vaccination impact the fate of vaccine-resistant strains

Vaccines are thought to be the best available solution for controlling the ongoing SARS-CoV-2 pandemic. However, the emergence of vaccine-resistant strains may come too rapidly for current vaccine developments to alleviate the health, economic and social consequences of the pandemic. To quantify and characterize the risk of such a scenario, we created a SIR-derived model with initial stochastic dynamics of the vaccine-resistant strain to study the probability of its emergence and establishment. Using parameters realistically resembling SARS-CoV-2 transmission, we model a wave-like pattern of the pandemic and consider the impact of the rate of vaccination and the strength of non-pharmaceutical intervention measures on the probability of emergence of a resistant strain. As expected, we found that a fast rate of vaccination decreases the probability of emergence of a resistant strain. Counterintuitively, when a relaxation of non-pharmaceutical interventions happened at a time when most individuals of the population have already been vaccinated the probability of emergence of a resistant strain was greatly increased. Consequently, we show that a period of transmission reduction close to the end of the vaccination campaign can substantially reduce the probability of resistant strain establishment. Our results suggest that policymakers and individuals should consider maintaining non-pharmaceutical interventions and transmission-reducing behaviours throughout the entire vaccination period.

Uncertainties in the Assessment of COVID-19 Risk: A Study of People’s Exposure to High-Risk Environments Using Individual-Level Activity Data

Based on different conceptualizations and measures of individual-level environmental exposure, this study examines how the uncertain geographic context problem (UGCoP) and the neighborhood effect averaging problem (NEAP) might affect the assessment of COVID-19 risk. Using the COVID-19 data on an open-access government Web site and the individual-level activity data of sixty confirmed COVID-19 cases (infected persons) in Hong Kong, we first represent COVID-19 risk environments using case-based and venues-based high-risk locations. The COVID-19 risk of each of the sixty selected cases is then evaluated by three approaches based on their exposures to the case-based or venues-based risk environments: the mobility-based approach, the residence-based approach, and the activity space–based approach. The results indicate that the UGCoP and the NEAP exist in the assessment of COVID-19 risk, which has significant implications: Ecological COVID-19 studies need to address the uncertainties due to the UGCoP and the NEAP by considering people’s daily mobility. Otherwise, ignoring peoples’ daily mobility and its interactions with complex and dynamic COVID-19 risk environments could lead to misleading results and misinform government nonpharmaceutical intervention measures.

Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread

Effective strategies to restrain COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing a mask is among the non-pharmaceutical intervention measures that can be used to cut the primary source of SARS-CoV2 droplets expelled by an infected individual. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over the nose and mouth in public. To contribute towards communal health, this paper aims to devise a highly accurate and real-time technique that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. The proposed technique is ensemble of one-stage and two-stage detectors to achieve low inference time and high accuracy. We start with ResNet50 as a baseline and applied the concept of transfer learning to fuse high-level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiment is conducted with three popular baseline models viz. ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique achieves high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model generates 11.07% and 6.44% higher precision and recall in mask detection when compared to the recent public baseline model published as RetinaFaceMask detector. The outstanding performance of the proposed model is highly suitable for video surveillance devices.

Network models and the interpretation of prolonged infection plateaus in the COVID19 pandemic

Non-pharmaceutical intervention measures, such as social distancing, have so far been the only means to slow the spread of SARS-CoV-2. In the United States, strict social distancing during the first wave of virus spread has resulted in different types of infection dynamics. In some states, such as New York, extensive infection spread was followed by a pronounced decline of infection levels. In other states, such as California, less infection spread occurred before strict social distancing, and a different pattern was observed. Instead of a pronounced infection decline, a long-lasting plateau is evident, characterized by similar daily new infection levels. Here we show that network models, in which individuals and their social contacts are explicitly tracked, can reproduce the plateau if network connections are cut due to social distancing measures. The reason is that in networks characterized by a 2D spatial structure, infection tends to spread quadratically with time, but as edges are randomly removed, the infection spreads along nearly one-dimensional infection “corridors”, resulting in plateau dynamics. Further, we show that plateau dynamics are observed only if interventions start sufficiently early; late intervention leads to a “peak and decay” pattern. Interestingly, the plateau dynamics are predicted to eventually transition into an infection decline phase without any further increase in social distancing measures. Additionally, the models suggest that a second wave becomes significantly less pronounced if social distancing is only relaxed once the dynamics have transitioned to the decline phase. The network models analyzed here allow us to interpret and reconcile different infection dynamics during social distancing observed in various US states.

On realized serial and generation intervals given control measures: The COVID-19 pandemic case.

The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation interval. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.