Modeling infectious disease dynamics

Capturing the Impact of Vaccination and Human Mobility on the Evolution of COVID-19 Pandemic

Studying the interplay among human mobility, viral transmission, and vaccination can provide significant insights into the factors driving a pandemic. Using the COVID-19 pandemic as a case study, this paper investigates how human mobility, population density, and vaccination affect viral transmission. We employ two data-driven approaches: correlation analysis and structural equation modeling (SEM). Firstly, the correlation analysis reveals a nonuniform relationship between human mobility and disease incidence during the six pandemic periods. The most significant changes in mobility occurred during the initial emergence phase and after the vaccine rollout. We found a positive correlation between mobility and case rates early in the pandemic, but later on, they were not positively correlated. Consequently, after the widespread availability of the vaccine, mobility returned to around % of pre-pandemic values as the case incidence decreased. Secondly, we employed SEM to investigate two key aspects. Initially, we explored the association between mobility and COVID-19 incidence during the lockdown and post-lockdown periods for Florida and cross-validated with California, Texas, and New York. Subsequently, we analyzed how vaccination directly and indirectly impacted disease transmission after it became widely available. The findings reveal that vaccination led to a significant drop in case numbers and an increase in mobility, which may have contributed to the subsequent epidemic wave.

Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease

The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population's temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23-1.58), and 1.43(1.26-1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure.

A framework for counterfactual analysis, strategy evaluation, and control of epidemics using reproduction number estimates

During pandemics, countries, regions, and communities develop various epidemic models to evaluate spread and guide mitigation policies. However, model uncertainties caused by complex transmission behaviors, contact-tracing networks, time-varying parameters, human factors, and limited data present significant challenges to model-based approaches. To address these issues, we propose a novel framework that centers around reproduction number estimates to perform counterfactual analysis, strategy evaluation, and feedback control of epidemics. The framework 1) introduces a mechanism to quantify the impact of the testing-for-isolation intervention strategy on the basic reproduction number. Building on this mechanism, the framework 2) proposes a method to reverse engineer the effective reproduction number under different strengths of the intervention strategy. In addition, based on the method that quantifies the impact of the testing-for-isolation strategy on the basic reproduction number, the framework 3) proposes a closed-loop control algorithm that uses the effective reproduction number both as feedback to indicate the severity of the spread and as the control goal to guide adjustments in the intensity of the intervention. We illustrate the framework, along with its three core methods, by addressing three key questions and validating its effectiveness using data collected during the COVID-19 pandemic at the University of Illinois Urbana-Champaign (UIUC) and Purdue University: 1) How severe would an outbreak have been without the implemented intervention strategies? 2) What impact would varying the intervention strength have had on an outbreak? 3) How can we adjust the intervention intensity based on the current state of an outbreak?

Comprehending symmetry in epidemiology: A review of analytical methods and insights from models of COVID-19, Ebola, Dengue, and Monkeypox

The challenge of developing comprehensive mathematical models for guiding public health initiatives in disease control is varied. Creating complex models is essential to understanding the mechanics of the spread of infectious diseases. We reviewed papers that synthesized various mathematical models and analytical methods applied in epidemiological studies with a focus on infectious diseases such as Severe Acute Respiratory Syndrome Coronavirus-2, Ebola, Dengue, and Monkeypox. We address past shortcomings, including difficulties in simulating population growth, treatment efficacy and data collection dependability. We recently came up with highly specific and cost-effective diagnostic techniques for early virus detection. This research includes stability analysis, geographical modeling, fractional calculus, new techniques, and validated solvers such as validating solver for parametric ordinary differential equation. The study examines the consequences of different models, equilibrium points, and stability through a thorough qualitative analysis, highlighting the reliability of fractional order derivatives in representing the dynamics of infectious diseases. Unlike standard integer-order approaches, fractional calculus captures the memory and hereditary aspects of disease processes, resulting in a more complex and realistic representation of disease dynamics. This study underlines the impact of public health measures and the critical importance of spatial modeling in detecting transmission zones and informing targeted interventions. The results highlight the need for ongoing financing for research, especially beyond the coronavirus, and address the difficulties in converting analytically complicated findings into practical public health recommendations. Overall, this review emphasizes that further research and innovation in these areas are crucial for addressing ongoing and future public health challenges.

Exploring the influence of environmental indicators and forecasting influenza incidence using ARIMAX models

Background

Influenza is a respiratory infection that poses a significant health burden worldwide. Environmental indicators, such as air pollutants and meteorological factors, play a role in the onset and propagation of influenza. Accurate predictions of influenza incidence and understanding the factors influencing it are crucial for public health interventions. Our study aims to investigate the impact of various environmental indicators on influenza incidence and apply the ARIMAX model to integrate these exogenous variables to enhance the accuracy of influenza incidence predictions.

Method

Descriptive statistics and time series analysis were employed to illustrate changes in influenza incidence, air pollutants, and meteorological indicators. Cross correlation function (CCF) was used to evaluate the correlation between environmental indicators and the influenza incidence. We used ARIMA and ARIMAX models to perform predictive analysis of influenza incidence.

Results

From January 2014 to September 2023, a total of 21,573 cases of influenza were reported in Fuzhou, with a noticeable year-by-year increase in incidence. The peak of influenza typically occurred around January each year. The results of CCF analysis showed that all 10 environmental indicators had a significant impact on the incidence of influenza. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model exhibited the best prediction performance, as indicated by the lowest AIC, AICc, and BIC values, which were 529.740, 530.360, and 542.910, respectively. The model achieved a fitting RMSE of 2.999 and a predicting RMSE of 12.033.

Conclusion

This study provides insights into the impact of environmental indicators on influenza incidence in Fuzhou. The ARIMAX(0, 0, 1) (1, 0, 0)12 with PM10(lag5) model could provide a scientific basis for formulating influenza control policies and public health interventions. Timely prediction of influenza incidence is essential for effective epidemic control strategies and minimizing disease transmission risks.

Modeling zoonotic and vector-borne viruses

The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.

Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.

Chimeric Forecasting: An experiment to leverage human judgment to improve forecasts of infectious disease using simulated surveillance data

Forecasts of infectious agents provide public health officials advanced warning about the intensity and timing of the spread of disease. Past work has found that accuracy and calibration of forecasts is weakest when attempting to predict an epidemic peak. Forecasts from a mechanistic model would be improved if there existed accurate information about the timing and intensity of an epidemic. We presented 3000 humans with simulated surveillance data about the number of incident hospitalizations from a current and two past seasons, and asked that they predict the peak time and intensity of the underlying epidemic. We found that in comparison to two control models, a model including human judgment produced more accurate forecasts of peak time and intensity of hospitalizations during an epidemic. Chimeric models have the potential to improve our ability to predict targets of public health interest which may in turn reduce infectious disease burden.

Self-reported Communicable Diseases and Associated Socio-demographic Status Among Ethnic Minority Populations in Vietnam

INTRODUCTION

This study was conducted to identify the self-reported communicable diseases (CDs) rate and associated factors among ethnic minority populations in Vietnam.

METHODS

We conducted a cross-sectional study of 6912 ethnic minority participants from 12 provinces located in four socioeconomic regions in Vietnam. A total of 4985 participants were included in the final analysis. We used a structured questionnaire to collect information on self-reported CDs and socio-demographic information.

RESULTS

The results showed that the prevalence of self-reported CDs was 5.7% (95% CI: 5.0-6.4%). Ethnicity was shown to have an independently significant correlation to self-reported CDs. The Cham Ninh Thuan, Tay, Dao and Gie Trieng ethnic populations had significantly higher odds of self-reported CDs than those of La Hu ethnicity (OR = 47.1, 6.3, 5.6, and 6.5, respectively). Older people and males had significantly higher odds of having CDs than younger and females.

CONCLUSION

Our findings recommend conducting ethnic-specific interventions to diminish the incidence of CDs.

Fundamental bound on epidemic overshoot in the SIR model

We derive an exact upper bound on the epidemic overshoot for the Kermack-McKendrick SIR model. This maximal overshoot value of 0.2984 · · · occurs at [Formula: see text]. In considering the utility of the notion of overshoot, a rudimentary analysis of data from the first wave of the COVID-19 pandemic in Manaus, Brazil highlights the public health hazard posed by overshoot for epidemics with R0 near 2. Using the general analysis framework presented within, we then consider more complex SIR models that incorporate vaccination.

Randomness accelerates the dynamic clearing process of the COVID-19 outbreaks in China

During the implementation of strong non-pharmaceutical interventions (NPIs), more than one hundred COVID-19 outbreaks induced by different strains in China were dynamically cleared in about 40 days, which presented the characteristics of small scale clustered outbreaks with low peak levels. To address how did randomness affect the dynamic clearing process, we derived an iterative stochastic difference equation for the number of newly reported cases based on the classical stochastic SIR model and calculate the stochastic control reproduction number (SCRN). Further, by employing the Bayesian technique, the change points of SCRNs have been estimated, which is an important prerequisite for determining the lengths of the exponential growth and decline phases. To reveal the influence of randomness on the dynamic zeroing process, we calculated the explicit expression of the mean first passage time (MFPT) during the decreasing phase using the relevant theory of first passage time (FPT), and the main results indicate that random noise can accelerate the dynamic zeroing process. This demonstrates that powerful NPI measures can rapidly reduce the number of infected people during the exponential decline phase, and enhanced randomness is conducive to dynamic zeroing, i.e. the greater the random noise, the shorter the average clearing time is. To confirm this, we chose 26 COVID-19 outbreaks in various provinces in China and fitted the data by estimating the parameters and change points. We then calculated the MFPTs, which were consistent with the actual duration of dynamic zeroing interventions.

Delay-differential SEIR modeling for improved modelling of infection dynamics

SEIR (Susceptible-Exposed-Infected-Recovered) approach is a classic modeling method that is frequently used to study infectious diseases. However, in the vast majority of such models transitions from one population group to another are described using the mass-action law. That causes inability to reproduce observable dynamics of an infection such as the incubation period or progression of the disease's symptoms. In this paper, we propose a new approach to simulate the epidemic dynamics based on a system of differential equations with time delays and instant transitions to approximate durations of transition processes more correctly and make model parameters more clear. The suggested approach can be applied not only to Covid-19 but also to the study of other infectious diseases. We utilized it in the development of the delay-based model of the COVID-19 pandemic in Germany and France. The model takes into account testing of different population groups, symptoms progression from mild to critical, vaccination, duration of protective immunity and new virus strains. The stringency index was used as a generalized characteristic of the non-pharmaceutical government interventions in corresponding countries to contain the virus spread. The parameter identifiability analysis demonstrated that the presented modeling approach enables to significantly reduce the number of parameters and make them more identifiable. Both models are publicly available.

A data-driven Markov process for infectious disease transmission

The 2019 coronavirus pandemic exudes public health and socio-economic burden globally, raising an unprecedented concern for infectious diseases. Thus, describing the infectious disease transmission process to design effective intervention measures and restrict its spread is a critical scientific issue. We propose a level-dependent Markov model with infinite state space to characterize viral disorders like COVID-19. The levels and states in this model represent the stages of outbreak development and the possible number of infectious disease patients. The transfer of states between levels reflects the explosive transmission process of infectious disease. A simulation method with heterogeneous infection is proposed to solve the model rapidly. After that, simulation experiments were conducted using MATLAB according to the reported data on COVID-19 published by Johns Hopkins. Comparing the simulation results with the actual situation shows that our proposed model can well capture the transmission dynamics of infectious diseases with and without imposed interventions and evaluate the effectiveness of intervention strategies. Further, the influence of model parameters on transmission dynamics is analyzed, which helps to develop reasonable intervention strategies. The proposed approach extends the theoretical study of mathematical modeling of infectious diseases and contributes to developing models that can describe an infinite number of infected persons.

Relative role of community transmission and campus contagion in driving the spread of SARS-CoV-2: Lessons from Princeton University

Mathematical models have played a crucial role in exploring and guiding pandemic responses. University campuses present a particularly well-documented case for institutional outbreaks, thereby providing a unique opportunity to understand detailed patterns of pathogen spread. Here, we present descriptive and modeling analyses of SARS-CoV-2 transmission on the Princeton University (PU) campus-this model was used throughout the pandemic to inform policy decisions and operational guidelines for the university campus. Epidemic patterns between the university campus and surrounding communities exhibit strong spatiotemporal correlations. Mathematical modeling analysis further suggests that the amount of on-campus transmission was likely limited during much of the wider pandemic until the end of 2021. Finally, we find that a superspreading event likely played a major role in driving the Omicron variant outbreak on the PU campus during the spring semester of the 2021-2022 academic year. Despite large numbers of cases on campus in this period, case levels in surrounding communities remained low, suggesting that there was little spillover transmission from campus to the local community.

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.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.

A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Reduction of Viral and Bacterial Activity by Using a Self-Powered Variable-Frequency Electrical Stimulation Device

Viruses and bacteria, which can rapidly spread through droplets and saliva, can have serious effects on people's health. Viral activity is traditionally inhibited using chemical substances, such as alcohol or bleach, or physical methods, such as thermal energy or ultraviolet-light irradiation. However, such methods cannot be used in many applications because they have certain disadvantages, such as causing eye or skin injuries. Therefore, in the present study, the electrical stimulation method is used to stimulate a virus, namely, coronavirus 229E, and two types of bacteria, namely, Escherichia coli and Staphylococcus aureus, to efficiently reduce their infectivity of healthy cells (such as the Vero E6 cell in a viral activity-inhibition experiment). The infectivity effects of the aforementioned virus and bacteria were examined under varying values of different electrical stimulation parameters, such as the stimulation current, frequency, and total stimulation time. The experimental results indicate that the activity of coronavirus 229E is considerably inhibited through direct-current pulse stimulation with a current of 25 mA and a frequency of 2 or 20 Hz. In addition, E. coli activity was reduced by nearly 80% in 10 s through alternating-current pulse stimulation with a current of 50 mA and a frequency of 25 Hz. Moreover, a self-powered electrical stimulation device was constructed in this study. This device consists of a solar panel and battery to generate small currents with variable frequencies, which has advantages of self-powered and variable frequencies, and the device can be utilized on desks, chairs, or elevator buttons for the inhibition of viral and bacterial activities.

Data Assimilation Predictive GAN (DA-PredGAN) Applied to a Spatio-Temporal Compartmental Model in Epidemiology

We propose a novel use of generative adversarial networks (GANs) (i) to make predictions in time (PredGAN) and (ii) to assimilate measurements (DA-PredGAN). In the latter case, we take advantage of the natural adjoint-like properties of generative models and the ability to simulate forwards and backwards in time. GANs have received much attention recently, after achieving excellent results for their generation of realistic-looking images. We wish to explore how this property translates to new applications in computational modelling and to exploit the adjoint-like properties for efficient data assimilation. We apply these methods to a compartmental model in epidemiology that is able to model space and time variations, and that mimics the spread of COVID-19 in an idealised town. To do this, the GAN is set within a reduced-order model, which uses a low-dimensional space for the spatial distribution of the simulation states. Then the GAN learns the evolution of the low-dimensional states over time. The results show that the proposed methods can accurately predict the evolution of the high-fidelity numerical simulation, and can efficiently assimilate observed data and determine the corresponding model parameters.

Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois

PURPOSE

Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection.

METHODS

We created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations.

RESULTS

From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21) and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of prepandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations.

CONCLUSIONS

In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission.

Effect of Human Behavior on the Evolution of Viral Strains During an Epidemic

It is well known in the literature that human behavior can change as a reaction to disease observed in others, and that such behavioral changes can be an important factor in the spread of an epidemic. It has been noted that human behavioral traits in disease avoidance are under selection in the presence of infectious diseases. Here, we explore a complementary trend: the pathogen itself might experience a force of selection to become less “visible,” or less “symptomatic,” in the presence of such human behavioral trends. Using a stochastic SIR agent-based model, we investigated the co-evolution of two viral strains with cross-immunity, where the resident strain is symptomatic while the mutant strain is asymptomatic. We assumed that individuals exercised self-regulated social distancing (SD) behavior if one of their neighbors was infected with a symptomatic strain. We observed that the proportion of asymptomatic carriers increased over time with a stronger effect corresponding to higher levels of self-regulated SD. Adding mandated SD made the effect more significant, while the existence of a time-delay between the onset of infection and the change of behavior reduced the advantage of the asymptomatic strain. These results were consistent under random geometric networks, scale-free networks, and a synthetic network that represented the social behavior of the residents of New Orleans.

A model of COVID-19 pandemic with vaccines and mutant viruses

This paper proposes a compartment model (SVEIHRM model) based on a system of ordinary differential equations to simulate the pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Emergence of mutant viruses gave rise to multiple peaks in the number of confirmed cases. Vaccine developers and WHO suggest individuals to receive multiple vaccinations (the primary and the secondary vaccinations and booster shots) to mitigate transmission of COVID-19. Taking this into account, we include compartments for multiple vaccinations and mutant viruses of COVID-19 in the model. In particular, our model considers breakthrough infection according to the antibody formation rate following multiple vaccinations. We obtain the effective reproduction numbers of the original virus, the Delta, and the Omicron variants by fitting this model to data in Korea. Additionally, we provide various simulations adjusting the daily vaccination rate and the timing of vaccination to investigate the effects of these two vaccine-related measures on the number of infected individuals. We also show that starting vaccinations early is the key to reduce the number of infected individuals. Delaying the start date requires increasing substantially the rate of vaccination to achieve similar target results. In the sensitivity analysis on the vaccination rate of Korean data, it is shown that a 10% increase (decrease) in vaccination rates can reduce (increase) the number of confirmed cases by 35.22% (82.82%), respectively.

Travel-related Importation and Exportation Risks of SARS-CoV-2 Omicron Variant in 367 Prefectures (Cities) - China, 2022

INTRODUCTION

Minimizing the importation and exportation risks of coronavirus disease 2019 (COVID-19) is a primary concern for sustaining the "Dynamic COVID-zero" strategy in China. Risk estimation is essential for cities to conduct before relaxing border control measures.

METHODS

Informed by the daily number of passengers traveling between 367 prefectures (cities) in China, this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks.

RESULTS

Under the transmission scenario (R₀ =5.49), this study estimated the cumulative case incidence of Changchun City, Jilin Province as 3,233 (95% confidence interval: 1,480, 4,986) before a lockdown on March 14, 2022, which is close to the 3,168 cases reported in real life by March 16, 2022. In a total of 367 prefectures (cities), 127 (35%) had high exportation risks according to the simulation and could transmit the disease to 50% of all other regions within a period from 17 to 94 days. The average time until a new infection arrives in a location in 1 of the 367 prefectures (cities) ranged from 26 to 101 days.

CONCLUSIONS

Estimating COVID-19 importation and exportation risks is necessary for preparedness, prevention, and control measures of COVID-19 - especially when new variants emerge.

Measuring the impact of social-distancing, testing, and undetected asymptomatic cases on the diffusion of COVID-19

The key to overcoming COVID-19 lies, arguably, in the diffusion process of confirmed cases. In view of this, this study has two main aims: first, to investigate the unique characteristics of COVID-19-for the existence of asymptomatic cases-and second, to determine the best strategy to suppress the diffusion of COVID-19. To this end, this study proposes a new compartmental model-the SICUR model-which can address undetected asymptomatic cases and considers the three main drivers of the diffusion of COVID-19: the degree of social distancing, the speed of testing, and the detection rate of infected cases. Taking each country's situation into account, it is suggested that susceptible cases can be classified into two categories based on their sources of occurrence: internal and external factors. The results show that the ratio of undetected asymptomatic cases to infected cases will, ceteris paribus, be 6.9% for South Korea and 22.4% for the United States. This study also quantitatively shows that to impede the diffusion of COVID-19: firstly, strong social distancing is necessary when the detection rate is high, and secondly, fast testing is effective when the detection rate is low.

The effect of information-driven resource allocation on the propagation of epidemic with incubation period

In the pandemic of COVID-19, there are exposed individuals who are infected but lack distinct clinical symptoms. In addition, the diffusion of related information drives aware individuals to spontaneously seek resources for protection. The special spreading characteristic and coevolution of different processes may induce unexpected spreading phenomena. Thus we construct a three-layered network framework to explore how information-driven resource allocation affects SEIS (susceptible-exposed-infected-susceptible) epidemic spreading. The analyses utilizing microscopic Markov chain approach reveal that the epidemic threshold depends on the topology structure of epidemic network and the processes of information diffusion and resource allocation. Conducting extensive Monte Carlo simulations, we find some crucial phenomena in the coevolution of information diffusion, resource allocation and epidemic spreading. Firstly, when E-state (exposed state, without symptoms) individuals are infectious, long incubation period results in more E-state individuals than I-state (infected state, with obvious symptoms) individuals. Besides, when E-state individuals have strong or weak infectious capacity, increasing incubation period has an opposite effect on epidemic propagation. Secondly, the short incubation period induces the first-order phase transition. But enhancing the efficacy of resources would convert the phase transition to a second-order type. Finally, comparing the coevolution in networks with different topologies, we find setting the epidemic layer as scale-free network can inhibit the spreading of the epidemic.

A method to predict the response to directional selection using a Kalman filter

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder's equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder's equation plus a bias term. Second, the method combines information from the breeder's equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder's equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder's equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.

A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility

This paper investigates the impact of human activity and mobility (HAM) in the spreading dynamics of an epidemic. Specifically, it explores the interconnections between HAM and its effect on the early spread of the COVID-19 virus. During the early stages of the pandemic, effective reproduction numbers exhibited a high correlation with human mobility patterns, leading to a hypothesis that the HAM system can be studied as a coupled system with disease spread dynamics. This study applies the generalized Koopman framework with control inputs to determine the nonlinear disease spread dynamics and the input-output characteristics as a locally linear controlled dynamical system. The approach solely relies on the snapshots of spatiotemporal data and does not require any knowledge of the system's underlying physical laws. We exploit the Koopman operator framework by utilizing the Hankel dynamic mode decomposition with Control (HDMDc) algorithm to obtain a linear disease spread model incorporating human mobility as a control input. The study demonstrated that the proposed methodology could capture the impact of local mobility on the early dynamics of the ongoing global pandemic. The obtained locally linear model can accurately forecast the number of new infections for various prediction windows ranging from two to four weeks. The study corroborates a leader-follower relationship between mobility and disease spread dynamics. In addition, the effect of delay embedding in the HDMDc algorithm is also investigated and reported. A case study was performed using COVID infection data from Florida, US, and HAM data extracted from Google community mobility data report.

COVID-19 and children: medical impact and collateral damage

Children mostly experience mild SARS-CoV-2 infections, but the extent of paediatric COVID-19 disease differs between geographical regions and the distinct pandemic waves. Not all infections in children are mild, some children even show a strong inflammatory reaction resulting in a multisystem inflammatory syndrome. The assessments of paediatric vaccination depend on the efficacy of protection conferred by vaccination, the risk of adverse reactions and whether children contribute to herd immunity against COVID-19. Children were also the target of consequential public health actions such as school closure which caused substantial harm to children (educational deficits, sociopsychological problems) and working parents. It is, therefore, important to understand the transmission dynamics of SARS-CoV-2 infections by children to assess the efficacy of school closures and paediatric vaccination. The societal restrictions to contain the COVID-19 pandemic had additional negative effects on children's health, such as missed routine vaccinations, nutritional deprivation and lesser mother-child medical care in developing countries causing increased child mortality as a collateral damage. In this complex epidemiological context, it is important to have an evidence-based approach to public health approaches. The present review summaries pertinent published data on the role of children in the pandemic, whether they are drivers or followers of the infection chains and whether they are (after elderlies) major sufferers or mere bystanders of the COVID-19 pandemic.

The changing epidemiology of SARS-CoV-2

We have come a long way since the start of the COVID-19 pandemic-from hoarding toilet paper and wiping down groceries to sending our children back to school and vaccinating billions. Over this period, the global community of epidemiologists and evolutionary biologists has also come a long way in understanding the complex and changing dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. In this Review, we retrace our steps through the questions that this community faced as the pandemic unfolded. We focus on the key roles that mathematical modeling and quantitative analyses of empirical data have played in allowing us to address these questions and ultimately to better understand and control the pandemic.

First-wave COVID-19 daily cases obey gamma law

Modelling how a pandemic is spreading over time is a challenging issue. The new coronavirus disease called COVID-19 does not escape this rule as it has embraced over two hundred countries. As for previous pandemics, several studies have attempted to model the occurrence of cases caused by COVID-19. However, no study has succeeded in accurately modelling the impact of the infectious agent. Here we show that COVID-19 daily case distribution in humans obeys a Gamma law, which two new parameters can describe without any adjustment. Though the Gamma law has been exploited for nearly two centuries to describe the statistical distribution of spatial or temporal quantities, the goodness-of-fit rationale using two or three parameters has remained enigmatic. The new Gamma law approach we demonstrate here emerges from actual data and sheds light on the underlying mechanisms of the observed phenomenon. This finding has promising applicability in the epidemiological domain and in all disciplines involving branching systems, for which our Gamma law approach may bring a solution to hitherto unsolved problems.

Gout during the SARS-CoV-2 pandemic: increased flares, urate levels and functional improvement

INTRODUCTION

Gout is the most common inflammatory arthritis, but was not considered in most COVID-19 and rheumatic diseases reports. Our aim was to describe changes in clinical data, treatment, function and quality of life for gout patients during COVID-19 pandemic.

METHODS

Prospective, descriptive and analytical study of 101 consecutive gout (ACR/EULAR 2015) patients from our clinic evaluated during pandemic by phone call (n=52) or phone call + face-to-face (n=68) that accepted to participate. Variables are demographics, clinical and treatment data, HAQ, EQ5D questionnaires and COVID-19-related data. Patients were divided in two groups: flare (n=36) or intercritical gout (n=65) also; available pre-pandemic data was obtained from 71 patients. Statistical analyses are X2, paired t-test and Wilcoxon test.

RESULTS

Included gout patients were males (95.8%), mean (SD) age 54.7 (10.7) years and disease duration 16.4 (9.8) years; 90% received allopurinol, 50% colchicine as prophylaxis and 25% suspended ≥ 1 medication. Comparison of pre-pandemic vs pandemic data showed > flares (4.4% vs 36%, p=0.01), more flares in the last 6 months: 0.31 (0.75) vs 1.71 (3.1), (p=0.004 and > urate levels: 5.6 (1.7)vs 6.7 (2.2) mg/dL, p=0.016. Unexpectedly, function and quality-of-life scores improved: HAQ score 0.65 (2.16) vs 0.12 (0.17), p= 0.001. Seven patients were COVID-19-confirmed cases; they had significantly more flares, higher urate levels and lower allopurinol doses and two died.

CONCLUSIONS

In gout patients, flares were 9 times more frequent during pandemic also, they had increased urate levels but led to an unexpected improvement in HAQ and functionality scores. Resilience and lifestyle changes in gout during COVID-19 pandemic require further studies. Key Points • COVID-19 pandemic is associated with 4 times more flares in gout patients. • Increased flares were also seen in previously well-controlled gout patients. • Increased serum urate levels were also found in gout patients during pandemic. • In our gout clinic, 8/101 patients were diagnosed as COVID-19+, and two of them died.

The impact of multiple non-pharmaceutical interventions on controlling COVID-19 outbreak without lockdown in Hong Kong: A modelling study

BACKGROUND

The 'third wave' of COVID-19 in Hong Kong, China was suppressed by non-pharmaceutical interventions (NPIs). Although social distancing regulations were quickly strengthened, the outbreak continued to grow, causing increasing delays in tracing and testing. Further regulations were introduced, plus 'targeted testing' services for at-risk groups. Estimating the impact of individual NPIs could provide lessons about how outbreaks can be controlled without radical lockdown. However, the changing delays in confirmation time challenge current modelling methods. We used a novel approach aimed at disentangling and quantifying the effects of individual interventions.

METHODS

We incorporated the causes of delays in tracing and testing (i.e. load-efficiency relationship) and the consequences from such delays (i.e. the proportion of un-traced cases and the proportion of traced-cases with confirmation delay) into a deterministic transmission model, which was fitted to the daily number of cases with and without an epi‑link (an indication of being contact-traced). The effect of each NPI was then calculated.

FINDINGS

The model estimated that after earlier relaxation of regulations, Re rose from 0.7 to 3.2. Restoration of social distancing to the previous state only reduced Re to 1.3, because of increased delay in confirmation caused by load on the contact-tracing system. However, Re decreased by 20.3% after the introduction of targeted testing and by 17.5% after extension of face-mask rules, reducing Re to 0.9 and suppressing the outbreak. The output of the model without incorporation of delay failed to capture important features of transmission and Re.

INTERPRETATION

Changing delay in confirmation has a significant impact on disease transmission and estimation of transmissibility. This leads to a clear recommendation that delay should be monitored and mitigated during outbreaks, and that delay dynamics should be incorporated into models to assess the effects of NPIs.

FUNDING

City University of Hong Kong and Health and Medical Research Fund.

Reducing bias in risk indices for COVID-19

Spatiotemporal modelling of infectious diseases such as coronavirus disease 2019 (COVID-19) involves using a variety of epidemiological metrics such as regional proportion of cases and/or regional positivity rates. Although observing changes of these indices over time is critical to estimate the regional disease burden, the dynamical properties of these measures, as well as crossrelationships, are usually not systematically given or explained. Here we provide a spatiotemporal framework composed of six commonly used and newly constructed epidemiological metrics and conduct a case study evaluation. We introduce a refined risk estimate that is biased neither by variation in population size nor by the spatial heterogeneity of testing. In particular, the proposed methodology would be useful for unbiased identification of time periods with elevated COVID-19 risk without sensitivity to spatial heterogeneity of neither population nor testing coverage.We offer a case study in Poland that shows improvement over the bias of currently used methods. Our results also provide insights regarding regional prioritisation of testing and the consequences of potential synchronisation of epidemics between regions. The approach should apply to other infectious diseases and other geographical areas.

Effect of 2021 assembly election in India on COVID-19 transmission

India is one of the countries in the world which is badly affected by the COVID-19 second wave. Assembly election in four states and a union territory of India was taken place during March-May 2021 when the COVID-19 second wave was close to its peak and affected a huge number of people. We studied the impact of assembly election on the effective contact rate and the effective reproduction number of COVID-19 using different epidemiological models like SIR, SIRD, and SEIR. We also modeled the effective reproduction number for all election-bound states using different mathematical functions. We separately studied the case of all election-bound states and found all the states showed a distinct increase in the effective contact rate and the effective reproduction number during the election-bound time and just after that compared to pre-election time. States, where elections were conducted in single-phase, showed less increase in the effective contact rate and the reproduction number. The election commission imposed extra measures from the first week of April 2021 to restrict big campaign rallies, meetings, and different political activities. The effective contact rate and the reproduction number showed a trend to decrease for few states due to the imposition of the restrictions. We also compared the effective contact rate, and the effective reproduction number of all election-bound states and the rest of India and found all the parameters related to the spread of virus for election-bound states are distinctly high compared to the rest of India.

Modeling the Waves of Covid-19

The challenges with modeling the spread of Covid-19 are its power-type growth during the middle stages of the waves with the exponents depending on time, and that the saturation of the waves is mainly due to the protective measures and other restriction mechanisms working in the same direction. The two-phase solution we propose for modeling the total number of detected cases of Covid-19 describes the actual curves for many its waves and in many countries almost with the accuracy of physics laws. Bessel functions play the key role in our approach. The differential equations we obtain are of universal type and can be used in behavioral psychology, invasion ecology (transient processes), etc. The initial transmission rate and the intensity of the restriction mechanisms are the key parameters. This theory provides a convincing explanation of the surprising uniformity of the Covid-19 waves in many places, and can be used for forecasting the epidemic spread. For instance, the early projections for the 3rd wave in the USA appeared sufficiently exact. The Delta-waves (2021) in India, South Africa, UK, and the Netherlands are discussed at the end.

COVID-19 vaccine confidence and hesitancy among health care workers: A cross-sectional survey from a MERS-CoV experienced nation

Objectives

This study aimed to identify coronavirus disease 2019 (COVID-19) vaccine perception, acceptance, confidence, hesitancy, and barriers among health care workers (HCWs).

Methods

An online national cross-sectional pilot-validated questionnaire was self-administered by HCWs in Saudi Arabia, which is a nation with MERS-CoV experience. The main outcome variable was HCWs’ acceptance of COVID-19 vaccine candidates. The factors associated with vaccination acceptance were identified through a logistic regression analysis, and the level of anxiety was measured using a validated instrument to measure general anxiety levels.

Results

Out of the 1512 HCWs who completed the study questionnaire—of which 62.4% were women—70% were willing to receive COVID-19 vaccines. A logistic regression analysis revealed that male HCWs (ORa = 1.551, 95% CI: 1.122–2.144), HCWs who believe in vaccine safety (ORa = 2.151; 95% CI: 1.708–2.708), HCWs who believe that COVID vaccines are the most likely way to stop the pandemic (ORa = 1.539; 95% CI: 1.259–1.881), and HCWs who rely on the Centers for Disease Control and Prevention website for COVID 19 updates (ORa = 1.505, 95% CI: 1.125–2.013) were significantly associated with reporting a willingness to be vaccinated. However, HCWs who believed that the vaccines were rushed without evidence-informed testing were found to be 60% less inclined to accept COVID-19 vaccines (ORa = 0.394, 95% CI: 0.298–0.522).

Conclusion

Most HCWs are willing to receive COVID-19 vaccines once they are available; the satisfactoriness of COVID-19 vaccination among HCWs is crucial because health professionals’ knowledge and confidence toward vaccines are important determining factors for not only their own vaccine acceptance but also recommendation for such vaccines to their patients.

Identifiability and predictability of integer- and fractional-order epidemiological models using physics-informed neural networks

We analyze a plurality of epidemiological models through the lens of physics-informed neural networks (PINNs) that enable us to identify time-dependent parameters and data-driven fractional differential operators. In particular, we consider several variations of the classical susceptible-infectious-removed (SIR) model by introducing more compartments and fractional-order and time-delay models. We report the results for the spread of COVID-19 in New York City, Rhode Island and Michigan states and Italy, by simultaneously inferring the unknown parameters and the unobserved dynamics. For integer-order and time-delay models, we fit the available data by identifying time-dependent parameters, which are represented by neural networks. In contrast, for fractional differential models, we fit the data by determining different time-dependent derivative orders for each compartment, which we represent by neural networks. We investigate the structural and practical identifiability of these unknown functions for different datasets, and quantify the uncertainty associated with neural networks and with control measures in forecasting the pandemic.

Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. IMPORTANCE COVID-19 involves a number of organ systems and can present with a wide range of symptoms. From how the virus infects cells to how it spreads between people, the available research suggests that these patterns are very similar to those seen in the closely related viruses SARS-CoV-1 and possibly Middle East respiratory syndrome-related CoV (MERS-CoV). Understanding the pathogenesis of the SARS-CoV-2 virus also contextualizes how the different biological systems affected by COVID-19 connect. Exploring the structure, phylogeny, and pathogenesis of the virus therefore helps to guide interpretation of the broader impacts of the virus on the human body and on human populations. For this reason, an in-depth exploration of viral mechanisms is critical to a robust understanding of SARS-CoV-2 and, potentially, future emergent human CoVs (HCoVs).

Braking Force Model on Virus Transmission to Evaluate Interventions Including the Administration of COVID-19 Vaccines - Worldwide, 2019-2021

Introduction

Assessing the effects of non-pharmaceutical interventions (NPIs) and vaccines on controlling the coronavirus disease 2019 (COVID-19) is key for each government to optimize the anti-contagion policy according to their situation.

Methods

We proposed the Braking Force Model on Virus Transmission to evaluate the validity and efficiency of NPIs and vaccines. This model classified the NPIs and the administration of vaccines at different effectiveness levels and forecasted the duration required to control the pandemic, providing an indication of the future trends of the pandemic wave.

Results

This model was applied to study the effectiveness of the most commonly used NPIs according to the historic pandemic waves in different countries and regions. It was found that when facing an outbreak, only strict lockdown would give efficient control of the pandemic; the other NPIs were insufficient to promptly and effectively reduce virus transmission. Meanwhile, our results showed that NPIs would likely only slow down the pandemic’s progression and maintain a low transmission level but fail to eradicate the disease. Only vaccination would likely have had a better chance of success in ending the pandemic.

Discussion

Based on the Braking Force Model, a pandemic control strategy framework has been devised for policymakers to determine the commencement and duration of appropriate interventions, with the aim of obtaining a balance between public health risk management and economic recovery.

Dynamic activity chain pattern estimation under mobility demand changes during COVID-19

During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

Game-theoretic modeling of collective decision making during epidemics

The spreading dynamics of an epidemic and the collective behavioral pattern of the population over which it spreads are deeply intertwined and the latter can critically shape the outcome of the former. Motivated by this, we design a parsimonious game-theoretic behavioral-epidemic model, in which an interplay of realistic factors shapes the coevolution of individual decision making and epidemics on a network. Although such a coevolution is deeply intertwined in the real world, existing models schematize population behavior as instantaneously reactive, thus being unable to capture human behavior in the long term. Our paradigm offers a unified framework to model and predict complex emergent phenomena, including successful collective responses, periodic oscillations, and resurgent epidemic outbreaks. The framework also allows us to provide analytical insights on the epidemic process and to assess the effectiveness of different policy interventions on ensuring a collective response that successfully eradicates the outbreak. Two case studies, inspired by real-world diseases, are presented to illustrate the potentialities of the proposed model.

Effects of B.1.1.7 and B.1.351 on COVID-19 Dynamics: A Campus Reopening Study

The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics.

Modeling neutral viral mutations in the spread of SARS-CoV-2 epidemics

Although traditional models of epidemic spreading focus on the number of infected, susceptible and recovered individuals, a lot of attention has been devoted to integrate epidemic models with population genetics. Here we develop an individual-based model for epidemic spreading on networks in which viruses are explicitly represented by finite chains of nucleotides that can mutate inside the host. Under the hypothesis of neutral evolution we compute analytically the average pairwise genetic distance between all infecting viruses over time. We also derive a mean-field version of this equation that can be added directly to compartmental models such as SIR or SEIR to estimate the genetic evolution. We compare our results with the inferred genetic evolution of SARS-CoV-2 at the beginning of the epidemic in China and found good agreement with the analytical solution of our model. Finally, using genetic distance as a proxy for different strains, we use numerical simulations to show that the lower the connectivity between communities, e.g., cities, the higher the probability of reinfection.

Seroprevalence of Severe Acute Respiratory Syndrome Coronavirus 2 Following the Largest Initial Epidemic Wave in the United States: Findings From New York City, 13 May to 21 July 2020

Background

New York City (NYC) was the U.S. epicenter of the Spring 2020 COVID-19 pandemic. We present seroprevalence of SARS-CoV-2 infection and correlates of seropositivity immediately after the first wave.

Methods

From a serosurvey of adult NYC residents (May 13-July 21, 2020), we calculated the prevalence of SARS-CoV-2 antibodies stratified by participant demographics, symptom history, health status, and employment industry. We used multivariable regression models to assess associations between participant characteristics and seropositivity.

Results

Seroprevalence among 45,367 participants was 23.6% (95% CI, 23.2%-24.0%). High seroprevalence (>30%) was observed among Black and Hispanic individuals, people from high poverty neighborhoods, and people in health care or essential worker industry sectors. COVID-19 symptom history was associated with seropositivity (adjusted relative risk=2.76; 95% CI, 2.65-2.88). Other risk factors included sex, age, race/ethnicity, residential area, employment sector, working outside the home, contact with a COVID-19 case, obesity, and increasing numbers of household members.

Conclusions

Based on a large serosurvey in a single U.S. jurisdiction, we estimate that just under one-quarter of NYC adults were infected in the first few months of the COVID-19 epidemic. Given disparities in infection risk, effective interventions for at-risk groups are needed during ongoing transmission.

Solar UV-B/A radiation is highly effective in inactivating SARS-CoV-2

Solar UV-C photons do not reach Earth's surface, but are known to be endowed with germicidal properties that are also effective on viruses. The effect of softer UV-B and UV-A photons, which copiously reach the Earth's surface, on viruses are instead little studied, particularly on single-stranded RNA viruses. Here we combine our measurements of the action spectrum of Covid-19 in response to UV light, Solar irradiation measurements on Earth during the SARS-CoV-2 pandemics, worldwide recorded Covid-19 mortality data and our "Solar-Pump" diffusive model of epidemics to show that (a) UV-B/A photons have a powerful virucidal effect on the single-stranded RNA virus Covid-19 and that (b) the Solar radiation that reaches temperate regions of the Earth at noon during summers, is sufficient to inactivate 63% of virions in open-space concentrations (1.5 × 10³ TCID₅₀/mL, higher than typical aerosol) in less than 2 min. We conclude that the characteristic seasonality imprint displayed world-wide by the SARS-Cov-2 mortality time-series throughout the diffusion of the outbreak (with temperate regions showing clear seasonal trends and equatorial regions suffering, on average, a systematically lower mortality), might have been efficiently set by the different intensity of UV-B/A Solar radiation hitting different Earth's locations at different times of the year. Our results suggest that Solar UV-B/A play an important role in planning strategies of confinement of the epidemics, which should be worked out and set up during spring/summer months and fully implemented during low-solar-irradiation periods.

Habitat loss and the risk of disease outbreak

Evidence suggests that emerging infectious diseases, such as COVID-19, originate from wildlife species, and that land-use change is an important pathway for pathogen transmission to humans. We first focus on zoonotic disease spillover and the rate at which primary human cases appear, demonstrating that a potential outbreak is directly related to the area of wildlife habitat. We then develop a model of the costs and benefits of land conversion that includes the effect of habitat size on the risk of disease outbreak. Our model and numerical simulations show that incorporating this risk requires more wildlife habitat conservation in the long run, and how much more should be conserved will depend on the initial habitat size. If the area is too small, then no conversion should take place. Any policy to control habitat loss, such as a tax imposed on the rents from converted land, should also vary with habitat area.

Bayesian back-calculation and nowcasting for line list data during the COVID-19 pandemic

Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.

A review and agenda for integrated disease models including social and behavioural factors

Social and behavioural factors are critical to the emergence, spread and containment of human disease, and are key determinants of the course, duration and outcomes of disease outbreaks. Recent epidemics of Ebola in West Africa and coronavirus disease 2019 (COVID-19) globally have reinforced the importance of developing infectious disease models that better integrate social and behavioural dynamics and theories. Meanwhile, the growth in capacity, coordination and prioritization of social science research and of risk communication and community engagement (RCCE) practice within the current pandemic response provides an opportunity for collaboration among epidemiological modellers, social scientists and RCCE practitioners towards a mutually beneficial research and practice agenda. Here, we provide a review of the current modelling methodologies and describe the challenges and opportunities for integrating them with social science research and RCCE practice. Finally, we set out an agenda for advancing transdisciplinary collaboration for integrated disease modelling and for more robust policy and practice for reducing disease transmission.

Data-driven methods for present and future pandemics: Monitoring, modelling and managing

This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.