Social distancing versus early detection and contacts tracing in epidemic management

Production optimization in the time of pandemic: an SIS-based optimal control model with protection effort and cost minimization

The COVID-19 pandemic wreaks havoc in supply chains by reducing the production capacity of some essential suppliers, closure of production facilities or the absence of infected workers. In this paper, we present three decision support models for a plant manager to help in deciding on (a) the level of protection of the workforce against the spread of the virus in the absence of regional protection measures, (b) on the duration of the protection, and (c) the level of protection of the workforce with regional protection measures enforced by health authorities. These decision models are based on a SIS epidemiological model which takes into account the possibility that a worker can infect others but also that even when recovered can be infected again. The first and third models prescribe how, in time, the protection effort in terms of prophylactic measures must be deployed. The second model extends the first one as it also determines the length the protection effort must be deployed. The proposed models have been applied to the case of a meat processing plant that must satisfy the demand of a large-scale retailer. Clearly, to achieve production targets and satisfy customers' demand, plants in this labor-intensive industry rely on the number of healthy workers and the service level of suppliers. Our results indicate that these models provide managers with the tools to understand and measure the impact of an infection on production and the corresponding cost. Along the way, this work illustrates the ripple effect as suppliers affected by the pandemic are unable to fulfill the processing plant requirements and so the retailer's orders. Our findings provide normative guidance for supply chain decision support systems under risk of pandemic induced disruptions using a quantitative model-based approach.

Evaluating Effects of Dynamic Interventions to Control COVID-19 Pandemic: A Case Study of Guangdong, China

The emergence of different virus variants, the rapidly changing epidemic, and demands for economic recovery all require continual adjustment and optimization of COVID-19 intervention policies. For the purpose, it is both important and necessary to evaluate the effectiveness of different policies already in-place, which is the basis for optimization. Although some scholars have used epidemiological models, such as susceptible-exposed-infected-removed (SEIR), to perform evaluation, they might be inaccurate because those models often ignore the time-varying nature of transmission rate. This study proposes a new scheme to evaluate the efficiency of dynamic COVID-19 interventions using a new model named as iLSEIR-DRAM. First, we improved the traditional LSEIR model by adopting a five-parameter logistic function β(t) to depict the key parameter of transmission rate. Then, we estimated the parameters by using an adaptive Markov Chain Monte Carlo (MCMC) algorithm, which combines delayed rejection and adaptive metropolis samplers (DRAM). Finally, we developed a new quantitative indicator to evaluate the efficiency of COVID-19 interventions, which is based on parameters in β(t) and considers both the decreasing degree of the transmission rate and the emerging time of the epidemic inflection point. This scheme was applied to seven cities in Guangdong Province. We found that the iLSEIR-DRAM model can retrace the COVID-19 transmission quite well, with the simulation accuracy being over 95% in all cities. The proposed indicator succeeds in evaluating the historical intervention efficiency and makes the efficiency comparable among different cities. The comparison results showed that the intervention policies implemented in Guangzhou is the most efficient, which is consistent with public awareness. The proposed scheme for efficiency evaluation in this study is easy to implement and may promote precise prevention and control of the COVID-19 epidemic.

Monitoring COVID-19 in Colombia during the first year of the pandemic

Background:

COVID-19 cases in Medellín, the second largest city in Colombia, were monitored during the first year of the pandemic using both mathematical models based on transmission theory and surveillance from each observed epidemic phase.

Design and Methods:

Expected cases were estimated using mandatory reporting data from Colombia’s national epidemiological surveillance system from March 7, 2020 to March 7, 2021. Initially, the range of daily expected cases was estimated using a Borel-Tanner stochastic model and a deterministic Susceptible-Infected-Removed (SIR) model. A subsequent expanded version of the SIR model was used to include asymptomatic cases, severe cases and deaths. The moving average, standard deviation, and goodness of fit of estimated cases relative to confirmed reported cases were assessed, and local transmission in Medellin was contrasted with national transmission in Colombia.

Results:

The initial phase was characterized by imported case detection and the later phase by community transmission and increases in case magnitude and severity. In the initial phase, a maximum range of expected cases was obtained based on the stochastic model, which even accounted for the reduction of new imported cases following the closure of international airports. The deterministic estimate achieved an adequate fit with respect to accumulated cases until the conclusion of the mandatory national quarantine and gradual reopening, when reported cases increased. The estimated new cases were reasonably fit with the maximum reported incidence.

Conclusion:

Adequate model fit was obtained with the reported data. This experience of monitoring epidemic trajectory can be extended using models adapted to local conditions.

Influence of state reopening policies in COVID-19 mortality

By the end of May 2020, all states in the US have eased their COVID-19 mitigation measures. Different states adopted markedly different policies and timing for reopening. An important question remains in how the relaxation of mitigation measures is related to the number of casualties. To address this question, we compare the actual data to a hypothetical case in which the mitigation measures are left intact using a projection of the data from before mitigation measures were eased. We find that different states have shown significant differences between the actual number of deaths and the projected figures within the present model. We relate these differences to the states different policies and reopening schedules. Our study provides a gauge for the effectiveness of the approaches by different state governments and can serve as a guide for implementing best policies in the future. According to the Pearson correlation coefficients we obtained, the face mask mandate has the strongest correlation with the death count than any other policies we considered.

Reflections throughout the COVID-19 Lockdown: What Do I Need for Successful Learning of Engineering?

The intention of this study was to identify the elements that engineering students consider fundamental for successful learning on engineering courses. The aim was to provide generic guidelines suitable for any engineering course with which the teaching may be adapted in the light of comments from students, while student learning improves. The abrupt transition from face-to-face to asynchronous online teaching due to the COVID-19 pandemic prompted reflection among students on both teaching methods. Students were invited to evaluate each method through a survey of open-ended questions, identifying useful elements for their learning. The survey was repeated over nine weeks, to obtain the views of students after they had accepted the change and had critically analyzed how to improve online teaching. A cross-coded qualitative and mixed (word counting) analysis showed that the explanation of engineering concepts should be organized, hierarchical, repetitive, and exemplified. Furthermore, the teacher should link all the activities and projects to the concepts explained and quickly solve any doubts that they raised. As a consequence of the online teaching resulting from COVID-19, the need of independent student learning and peer support was also very evident. Teaching functions are essential on engineering courses, as teachers have to explain the overall concepts carefully, identify the key concepts, and demonstrate their industrial and professional applications. Furthermore, teaching methodologies that balance these aspects with autonomy and peer support for learning on engineering courses should be promoted.

The first wave of the SARS-CoV-2 epidemic in Tuscany (Italy): A SI2R2D compartmental model with uncertainty evaluation

With the aim of studying the spread of the SARS-CoV-2 infection in the Tuscany region of Italy during the first epidemic wave (February-June 2020), we define a compartmental model that accounts for both detected and undetected infections and assumes that only notified cases can die. We estimate the infection fatality rate, the case fatality rate, and the basic reproduction number, modeled as a time-varying function, by calibrating on the cumulative daily number of observed deaths and notified infected, after fixing to plausible values the other model parameters to assure identifiability. The confidence intervals are estimated by a parametric bootstrap procedure and a Global Sensitivity Analysis is performed to assess the sensitivity of the estimates to changes in the values of the fixed parameters. According to our results, the basic reproduction number drops from an initial value of 6.055 to 0 at the end of the national lockdown, then it grows again, but remaining under 1. At the beginning of the epidemic, the case and the infection fatality rates are estimated to be 13.1% and 2.3%, respectively. Among the parameters considered as fixed, the average time from infection to recovery for the not notified infected appears to be the most impacting one on the model estimates. The probability for an infected to be notified has a relevant impact on the infection fatality rate and on the shape of the epidemic curve. This stresses the need of collecting information on these parameters to better understand the phenomenon and get reliable predictions.

The Outbreak of the COVID-19 Pandemic and its Social Impact on Education: Were Engineering Teachers Ready to Teach Online?

The major impacts of the COVID-19 pandemic are still affecting all social dimensions. Its specific impact on education is extensive and quite evident in the adaptation from Face-to-Face (F2F) teaching to online methodologies throughout the first wave of the pandemic and the strict rules on lockdown. As lesson formats changed radically, the relevance of evaluating student on-line learning processes in university degrees throughout this period became clear. For this purpose, the perceptions of engineering students towards five specific course units forming part of engineering degree courses at the University of Burgos, Spain, were evaluated to assess the quality of the online teaching they received. Comparisons were also drawn with their perceptions of the F2F teaching of the course units prior to the outbreak of the pandemic. According to the students' perceptions, the teachers possessed the technical knowledge, the social skills, and the personal capabilities (empathy and understanding of the at times troubled situation of each student) for a very abrupt adaptation of their courses to an online methodology. The shortcomings of the online teaching were related to its particularities and each teacher's personality traits. Overall, engineering teachers appeared well prepared for a situation of these characteristics and, if similar online teaching scenarios were ever repeated, the quality of engineering teaching appears to be guaranteed.

Effect of mitigation measures on the spreading of COVID-19 in hard-hit states in the U.S

State government-mandated social distancing measures have helped to slow the growth of the COVID-19 pandemic in the United States. Many of the current predictive models of the development of COVID-19, especially after mitigation efforts, partially rely on extrapolations from data collected in other countries. Since most states enacted stay-at-home orders towards the end of March, the resulting effects of social distancing should be reflected in the death and infection counts by the end of April. Using the data available through April 25th, we investigate the change in the infection rate due to the mitigation efforts and project death and infection counts through September 2020 for some of the most heavily impacted states: New York, New Jersey, Michigan, Massachusetts, Illinois, and Louisiana. We find that with the current mitigation efforts, five of those six states have reduced their base reproduction number to a value less than one, stopping the exponential growth of the pandemic. We also project different scenarios after the mitigation is relaxed.

How to reduce epidemic peaks keeping under control the time-span of the epidemic

One of the main challenges of the measures against the COVID-19 epidemic is to reduce the amplitude of the epidemic peak without increasing without control its timescale. We investigate this problem using the SIR model for the epidemic dynamics, for which reduction of the epidemic peak I_P can be achieved only at the price of increasing the time t_P of its occurrence and its entire time-span t_E . By means of a time reparametrization we linearize the equations for the SIR dynamics. This allows us to solve exactly the dynamics in the time domain and to derive the scaling behaviour of the size, the timescale and the speed of the epidemics, by reducing the infection rate α and by increasing the removal rate β by a factor of λ. We show that for a given value of the size (I_P , the total, I_E and average I ^ P number of infected), its occurrence time t_P and entire time-span t_E can be reduced by a factor 1/λ if the reduction of I is achieved by increasing the removal rate instead of reducing the infection rate. Thus, epidemic containment measures based on tracing, early detection followed by prompt isolation of infected individuals are more efficient than those based on social distancing. We apply our results to the COVID-19 epidemic in Northern Italy. We show that the peak time t_P and the entire time span t_E could have been reduced by a factor 0.9 ≤ 1/λ ≤ 0.34 with containment measures focused on increasing β instead of reducing α.