Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation.

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling.

High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making - Wuhan City, Hubei Province, China, January 1-February 29, 2020.

Introduction: High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019 (COVID-19), resident behavior, and interventions, which could be used to inform policy responses for future prevention and control. Methods: We obtained high-resolution human mobility data and epidemiological data at the community level. We propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal (SEPIR) compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of Wuhan. Additionally, we will assess the effectiveness of the interventions implemented in the smallest administrative units (subdistricts) during the lockdown. Results: In the Wuhan epidemic of March 2020, intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict transmission. After the city was closed, this ratio increased to 199 times. The main transmission path was dominated by population activity during peak evening hours. Discussion: Restricting the movement of people within cities is an essential measure for controlling the spread of COVID-19. However, it is difficult to contain intra-street transmission solely through city-wide mobility restriction policies. This can only be accomplished by quarantining communities or buildings with confirmed cases, and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.

Stability analysis of a model of epidemic dynamics with nonlinear feedback producing recurrent infection waves

Complex dynamics characterizing human behavior in an epidemiological scenario can be modeled via a system of ordinary differential equations starting from a simple SIR (susceptible–infected–recovered) model. Here we propose a nonlinear mathematical model that describes the evolution in time of susceptible, infected and hospitalized individuals. A new variable that reflects the society's "memory" of the severity of the epidemic is introduced, and this variable feeds back on the transmission rate of the disease. The nonlinear transmission rate reflects the fact that changes (e.g., an increase) in the number of hospitalized individuals can influence the behavior of society and individuals, which would affect (reduce) the probability of transmission. Differently from the standard SIR model, the nonlinear transmission rate may lead to complex dynamics with oscillatory solutions due to a Hopf bifurcation. Such oscillations correspond to recurrent infection waves. Using two parameter bifurcation diagrams we investigate the parameter space of the model. Finally, we report two examples on how the multiple infection waves present for the COVID-19 pandemic can be fitted by our model. [ FROM AUTHOR]

Fine-Grained Prediction and Control of Covid-19 Pandemic in a City: Application to Post-Initial Stages

Predicting the evolution of the Covid-19 pandemic during its early phases was relatively easy as its dynamics were governed by few influencing factors that included a single dominant virus variant and the demographic characteristics of a given area. Several models based on a wide variety of techniques were developed for this purpose. Their prediction accuracy started deteriorating as the number of influencing factors and their interrelationships grew over time. With the pandemic evolving in a highly heterogeneous way across individual countries, states, and even individual cities, there emerged a need for a contextual and fine-grained understanding of the pandemic to come up with effective means of pandemic control. This paper presents a fine-grained model for predicting and controlling Covid-19 in a large city. Our approach borrows ideas from complex adaptive system-of-systems paradigm and adopts a concept of agent as the core modeling ion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Stability analysis of a model of epidemic dynamics with nonlinear feedback producing recurrent infection waves

Complex dynamics characterizing human behavior in an epidemiological scenario can be modeled via a system of ordinary differential equations starting from a simple SIR (susceptible-infected-recovered) model. Here we propose a nonlinear mathematical model that describes the evolution in time of susceptible, infected and hospitalized individuals. A new variable that reflects the society’s “memory” of the severity of the epidemic is introduced, and this variable feeds back on the transmission rate of the disease. The nonlinear transmission rate reflects the fact that changes (e.g., an increase) in the number of hospitalized individuals can influence the behavior of society and individuals, which would affect (reduce) the probability of transmission. Differently from the standard SIR model, the nonlinear transmission rate may lead to complex dynamics with oscillatory solutions due to a Hopf bifurcation. Such oscillations correspond to recurrent infection waves. Using two parameter bifurcation diagrams we investigate the parameter space of the model. Finally, we report two examples on how the multiple infection waves present for the COVID-19 pandemic can be fitted by our model.

From Policy to Prediction: Forecasting COVID-19 Dynamics Under Imperfect Vaccination.

Understanding the joint impact of vaccination and non-pharmaceutical interventions on COVID-19 development is important for making public health decisions that control the pandemic. Recently, we created a method in forecasting the daily number of confirmed cases of infectious diseases by combining a mechanistic ordinary differential equation (ODE) model for infectious classes and a generalized boosting machine learning model (GBM) for predicting how public health policies and mobility data affect the transmission rate in the ODE model (Wang et al. in Bull Math Biol 84:57, 2022). In this paper, we extend the method to the post-vaccination period, accordingly obtain a retrospective forecast of COVID-19 daily confirmed cases in the US, and identify the relative influence of the policies used as the predictor variables. In particular, our ODE model contains both partially and fully vaccinated compartments and accounts for the breakthrough cases, that is, vaccinated individuals can still get infected. Our results indicate that the inclusion of data on non-pharmaceutical interventions can significantly improve the accuracy of the predictions. With the use of policy data, the model predicts the number of daily infected cases up to 35 days in the future, with an average mean absolute percentage error of [Formula: see text], which is further improved to [Formula: see text] if combined with human mobility data. Moreover, the most influential predictor variables are the policies of restrictions on gatherings, testing and school closing. The modeling approach used in this work can help policymakers design control measures as variant strains threaten public health in the future.

Development of an Explicit Agent-Based Simulation Toolkit for Opening of Schools: An Implementation of COMOKIT for Universities in the Philippines

Since the start of the pandemic in early 2020, there have been numerous studies related to the design and use of disease models to aid in understanding the transmission dynamics of COVID-19. Output of these models provide pertinent input to policies regarding restricting or relaxing movements of a population. Perhaps the most widely used class of models for COVID-19 disease transmission is the compartmental model. It is a population model that assumes homogeneous mixing, which means that each individual has the same likelihood of contact with the rest of the population. Inspite of this limitation, the approach has been effective in forecasting the number of cases based on simulated scenarios. With the shift from nationwide lockdowns to granular lockdown as well as gradual opening of limited face to face classes, there is a need to consider other models that assume heterogeneity as reflected in individual behaviors and spatial containment strategies in smaller spaces such as buildings. In this study, we use the COVID-19 Modeling Kit (COMOKIT, 2020) as a basis for the inclusion of individual and spatial components in the analysis. Specifically, we derive a version of COMOKIT specific to university setting. The model is an agent-based, spatially explicit model with the inclusion of individual epidemiological and behavior parameters to show evidence of which behavioral and non-pharmaceutical interventions lead to reduced transmission over a given period of time. The simulation environment is set up to accommodate the a) minimum number of persons required in a closed environment including classrooms, offices, study spaces, laboratories, cafeteria, prayer room and bookstore, b) parameters on viral load per building or office, and c) percentage of undetected positive cases going on campus. The model incorporates the following interventions: a) compliance to health protocol, in particular compliance to wearing masks, b) vaccine coverage, that is, the percentage distribution of single dose, two doses and booster, c) distribution of individuals into batches for alternating schedules. For mask compliance, as expected, results showed that 100% compliance resulted to lowest number of cases after 120 days, followed by 75% compliance and highest number of cases for 50% compliance. For vaccine coverage, results showed that booster shots play a significant role in lowering the number of cases. Specifically, those who are fully vaccinated (2 doses) and 100% boosted produce the lowest number of cases, followed by the 50% of the population fully vaccinated and have had their booster shots. Intervals of no onsite work or class in between weeks that have onsite classes produce the lowest number of cases. The best scenario is combining the three interventions with 100% compliance to mask wearing, 100% fully vaccinated with booster, and having two batches or groups with interval of no onsite classes. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

VACCINATION STRATEGIES THROUGH INTRA–COMPARTMENTAL DYNAMICS

We present a new epidemic model highlighting the roles of the immunization time and concurrent use of different vaccines in a vaccination campaign. To this aim, we introduce new intra-compartmental dynamics, a procedure that can be extended to various other situations, as detailed through specific case studies considered herein, where the dynamics within compartments are present and influence the whole evolution. © 2022, American Institute of Mathematical Sciences. All rights reserved.

A multi-strain epidemic model for COVID-19 with infected and asymptomatic cases: Application to French data.

Many SARS-CoV-2 variants have appeared over the last months, and many more will continue to appear. Understanding the competition between these different variants could help make future predictions on the evolution of epidemics. In this study we use a mathematical model to investigate the impact of three different SARS-CoV-2 variants on the spread of COVID-19 across France, between January-May 2021 (before vaccination was extended to the full population). To this end, we use the data from Geodes (produced by Public Health France) and a particle swarm optimisation algorithm, to estimate the model parameters and further calculate a value for the basic reproduction number R0. Sensitivity and uncertainty analysis is then used to better understand the impact of estimated parameter values on the number of infections leading to both symptomatic and asymptomatic individuals. The results confirmed that, as expected, the alpha, beta and gamma variants are more transmissible than the original viral strain. In addition, the sensitivity results showed that the beta/gamma variants could have lead to a larger number of infections in France (of both symptomatic and asymptomatic people).

A comparative study for predictive monitoring of COVID-19 pandemic.

COVID-19 pandemic caused by novel coronavirus (SARS-CoV-2) crippled the world economy and engendered irreparable damages to the lives and health of millions. To control the spread of the disease, it is important to make appropriate policy decisions at the right time. This can be facilitated by a robust mathematical model that can forecast the prevalence and incidence of COVID-19 with greater accuracy. This study presents an optimized ARIMA model to forecast COVID-19 cases. The proposed method first obtains a trend of the COVID-19 data using a low-pass Gaussian filter and then predicts/forecasts data using the ARIMA model. We benchmarked the optimized ARIMA model for 7-days and 14-days forecasting against five forecasting strategies used recently on the COVID-19 data. These include the auto-regressive integrated moving average (ARIMA) model, susceptible-infected-removed (SIR) model, composite Gaussian growth model, composite Logistic growth model, and dictionary learning-based model. We have considered the daily infected cases, cumulative death cases, and cumulative recovered cases of the COVID-19 data of the ten most affected countries in the world, including India, USA, UK, Russia, Brazil, Germany, France, Italy, Turkey, and Colombia. The proposed algorithm outperforms the existing models on the data of most of the countries considered in this study.

Organoid and microfluidics-based platforms for drug screening in COVID-19.

Proposing efficient prophylactic and therapeutic strategies for coronavirus 2019 (COVID-19) requires precise knowledge of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogenesis. An array of platforms, including organoids and microfluidic devices, have provided a basis for studies of SARS-CoV-2. Here, we summarize available models as well as novel drug screening approaches, from simple to more advanced platforms. Notably, organoids and microfluidic devices offer promising perspectives for the clinical translation of basic science, such as screening therapeutics candidates. Overall, modifying these advanced micro and macro 3D platforms for disease modeling and combining them with recent advances in drug screening has significant potential for the discovery of novel potent drugs against COVID-19.

Pan-African evolution of within- and between-country COVID-19 dynamics.

The coronavirus disease 2019 (COVID-19) pandemic is heterogeneous throughout Africa and threatening millions of lives. Surveillance and short-term modeling forecasts are critical to provide timely information for decisions on control strategies. We created a strategy that helps predict the country-level case occurrences based on cases within or external to a country throughout the entire African continent, parameterized by socioeconomic and geoeconomic variations and the lagged effects of social policy and meteorological history. We observed the effect of the Human Development Index, containment policies, testing capacity, specific humidity, temperature, and landlocked status of countries on the local within-country and external between-country transmission. One-week forecasts of case numbers from the model were driven by the quality of the reported data. Seeking equitable behavioral and social interventions, balanced with coordinated country-specific strategies in infection suppression, should be a continental priority to control the COVID-19 pandemic in Africa.

Nowcasting and Forecasting the Spread of COVID-19 and Healthcare Demand in Turkey, a Modeling Study.

Background: This study aims to estimate the total number of infected people, evaluate the effects of NPIs on the healthcare system, and predict the expected number of cases, deaths, hospitalizations due to COVID-19 in Turkey. Methods: This study was carried out according to three dimensions. In the first, the actual number of infected people was estimated. In the second, the expected total numbers of infected people, deaths, hospitalizations have been predicted in the case of no intervention. In the third, the distribution of the expected number of infected people and deaths, and ICU and non-ICU bed needs over time has been predicted via a SEIR-based simulator (TURKSAS) in four scenarios. Results: According to the number of deaths, the estimated number of infected people in Turkey on March 21 was 123,030. In the case of no intervention the expected number of infected people is 72,091,595 and deaths is 445,956, the attack rate is 88.1%, and the mortality ratio is 0.54%. The ICU bed capacity in Turkey is expected to be exceeded by 4.4-fold and non-ICU bed capacity by 3.21-fold. In the second and third scenarios compliance with NPIs makes a difference of 94,303 expected deaths. In both scenarios, the predicted peak value of occupied ICU and non-ICU beds remains below Turkey's capacity. Discussion: Predictions show that around 16 million people can be prevented from being infected and 94,000 deaths can be prevented by full compliance with the measures taken. Modeling epidemics and establishing decision support systems is an important requirement.

A quantitative framework for exploring exit strategies from the COVID-19 lockdown.

Following the highly restrictive measures adopted by many countries for combating the current pandemic, the number of individuals infected by SARS-CoV-2 and the associated number of deaths steadily decreased. This fact, together with the impossibility of maintaining the lockdown indefinitely, raises the crucial question of whether it is possible to design an exit strategy based on quantitative analysis. Guided by rigorous mathematical results, we show that this is indeed possible: we present a robust numerical algorithm which can compute the cumulative number of deaths that will occur as a result of increasing the number of contacts by a given multiple, using as input only the most reliable of all data available during the lockdown, namely the cumulative number of deaths.

An age and space structured SIR model describing the Covid-19 pandemic.

We present an epidemic model capable of describing key features of the Covid-19 pandemic. While capturing several qualitative properties of the virus spreading, it allows to compute the basic reproduction number, the number of deaths due to the virus and various other statistics. Numerical integrations are used to illustrate the adherence of the evolutions described by the model to specific well known real features of the present pandemic. In particular, this model is consistent with the well known relevance of quarantine, shows the dramatic role of care houses and accounts for the increase in the death toll when spatial movements are not constrained. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13362-020-00090-4) contains supplementary material.