Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

Determining the best mathematical model for implementation of non-pharmaceutical interventions

At the onset of the SARS-CoV-2 pandemic in early 2020, only non-pharmaceutical interventions (NPIs) were available to stem the spread of the infection. Much of the early interventions in the US were applied at a state level, with varying levels of strictness and compliance. While NPIs clearly slowed the rate of transmission, it is not clear how these changes are best incorporated into epidemiological models. In order to characterize the effects of early preventative measures, we use a Susceptible-Exposed-Infected-Recovered (SEIR) model and cumulative case counts from US states to analyze the effect of lockdown measures. We test four transition models to simulate the change in transmission rate: instantaneous, linear, exponential, and logarithmic. We find that of the four models examined here, the exponential transition best represents the change in the transmission rate due to implementation of NPIs in the most states, followed by the logistic transition model. The instantaneous and linear models generally lead to poor fits and are the best transition models for the fewest states.

Dynamic transmission modeling of COVID-19 to support decision-making in Brazil: A scoping review in the pre-vaccine era

Brazil was one of the countries most affected during the first year of the COVID-19 pandemic, in a pre-vaccine era, and mathematical and statistical models were used in decision-making and public policies to mitigate and suppress SARS-CoV-2 dispersion. In this article, we intend to overview the modeling for COVID-19 in Brazil, focusing on the first 18 months of the pandemic. We conducted a scoping review and searched for studies on infectious disease modeling methods in peer-reviewed journals and gray literature, published between January 01, 2020, and June 2, 2021, reporting real-world or scenario-based COVID-19 modeling for Brazil. We included 81 studies, most corresponding to published articles produced in Brazilian institutions. The models were dynamic and deterministic in the majority. The predominant model type was compartmental, but other models were also found. The main modeling objectives were to analyze epidemiological scenarios (testing interventions' effectiveness) and to project short and long-term predictions, while few articles performed economic impact analysis. Estimations of the R0 and transmission rates or projections regarding the course of the epidemic figured as major, especially at the beginning of the crisis. However, several other outputs were forecasted, such as the isolation/quarantine effect on transmission, hospital facilities required, secondary cases caused by infected children, and the economic effects of the pandemic. This study reveals numerous articles with shared objectives and similar methods and data sources. We observed a deficiency in addressing social inequities in the Brazilian context within the utilized models, which may also be expected in several low- and middle-income countries with significant social disparities. We conclude that the models were of great relevance in the pandemic scenario of COVID-19. Nevertheless, efforts could be better planned and executed with improved institutional organization, dialogue among research groups, increased interaction between modelers and epidemiologists, and establishment of a sustainable cooperation network.

A novel queue-based stochastic epidemic model with adaptive stabilising control

The main objective of this paper is to propose a novel SEIR stochastic epidemic model. A distinguishing feature of this new model is that it allows us to consider a setup under general latency and infectious period distributions. To some extent, queuing systems with infinitely many servers and a Markov chain with time-varying transition rate comprise the very technical underpinning of the paper. Although more general, the Markov chain is as tractable as previous models for exponentially distributed latency and infection periods. It is also significantly more straightforward and tractable than semi-Markov models with a similar level of generality. Based on stochastic stability, we derive a sufficient condition for a shrinking epidemic regarding the queuing system's occupation rate that drives the dynamics. Relying on this condition, we propose a class of ad-hoc stabilising mitigation strategies that seek to keep a balanced occupation rate after a prescribed mitigation-free period. We validate the approach in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil, and assess the effect of different stabilising strategies in the latter setting. Results suggest that the proposed approach can curb the epidemic with various occupation rate levels if the mitigation is timely.

A hybrid compartmental model with a case study of COVID-19 in Great Britain and Israel

Given the severe impact of COVID-19 on several societal levels, it is of crucial importance to model the impact of restriction measures on the pandemic evolution, so that governments are able to make informed decisions. Even though there have been countless attempts to propose diverse models since the rise of the outbreak, the increase in data availability and start of vaccination campaigns calls for updated models and studies. Furthermore, most of the works are focused on a very particular place or application and we strive to attain a more general model, resorting to data from different countries. In particular, we compare Great Britain and Israel, two highly different scenarios in terms of vaccination plans and social structure. We build a network-based model, complex enough to model different scenarios of government-mandated restrictions, but generic enough to be applied to any population. To ease the computational load we propose a decomposition strategy for our model.

Safety of the Fiocruz ChAdOx COVID-19 vaccine used in a mass vaccination campaign in Botucatu, Brazil

INTRODUCTION

Brazil has been at the core of the COVID-19 pandemic, with the second-highest death toll worldwide. A mass vaccination campaign was initiated on May 16th, 2021, in Botucatu, Brazil, where two doses of ChadOx1-nCoV19 were offered 12 weeks apart to all 18-60- year-olds. This context offers a unique opportunity to study the vaccine safety during a mass campaign.

METHODS

The first and second doses of the vaccine were administered in May and August 2021, respectively. Emergency room (ER) and hospitalization records were obtained from the Hospital das Clínicas da Faculdade de Medicina de Botucatu for six weeks before and six weeks after the first and second doses, from 4 April to 19 September 2021. Diagnoses with COVID-19-related ICD codes were excluded to distinguish any trends resulting from the COVID-19 pandemic. ER and hospital visits during the two time periods were compared, including an ICD code comparison, to identify any changes in disease distributions. Data were scanned for a defined list of Adverse Events of Special Interest (AESIs), as presented by the Safety Platform for Emergency Vaccines.

RESULTS AND DISCUSSION

A total of 77,683 and 74,051 subjects received dose 1 and dose 2 of ChadOx1-nCoV19, respectively. Vaccination was well tolerated and not associated with any major safety concerns. Increases in ER visits 1 week following both doses were primarily seen in ICD codes related to non-serious side effects of the vaccine, including vaccination site pain and other local events. The neurological AESIs identified (2 of 3 cases of multiple sclerosis) were relapses of a pre-existing condition. One potentially serious hospitalization event for Bell's palsy had onset before vaccination with dose 1, in a patient who also had a viral infection of the central nervous system. There was no myocarditis, pericarditis cases, or vaccine-related increases in thromboembolic events.

COVID- 19 and human right to food: lived experiences of the urban poor in Kenya with the impacts of government's response measures, a participatory qualitative study

BACKGROUND

Globally, governments put in place measures to curb the spread of COVID-19. Information on the effects of these measures on the urban poor is limited. This study aimed to explore the lived experiences of the urban poor in Kenya in the context of government's COVID-19 response measures and its impact on the human right to food.

METHODS

A qualitative study was conducted in two informal settlements in Nairobi between January and March 2021. Analysis draws on eight focus group discussions, eight in-depth interviews, 12 key informant interviews, two photovoice sessions and three digital storytelling sessions. Phenomenology was applied to understand an individual's lived experiences with the human right to food during COVID - 19. Thematic analysis was performed using NVIVO software.

RESULTS

The human right to food was affected in various ways. Many people lost their livelihoods, affecting affordability of food, due to response measures such as social distancing, curfew, and lockdown. The food supply chain was disrupted causing limited availability and access to affordable, safe, adequate, and nutritious food. Consequently, hunger and an increased consumption of low-quality food was reported. Social protection measures were instituted. However, these were inadequate and marred by irregularities. Some households resorted to scavenging food from dumpsites, skipping meals, sex-work, urban-rural migration and depending on food donations to survive. On the positive side, some households resorted to progressive measures such as urban farming and food sharing in the community. Generally, the response measures could have been more sensitive to the human rights of the urban poor.

CONCLUSIONS

The government's COVID-19 restrictive measures exacerbated the already existing vulnerability of the urban poor to food insecurity and violated their human right to food. Future response measures should be executed in ways that respect the human right to food and protect marginalized people from resultant vulnerabilities.

An epidemiology-based model for the operational allocation of COVID-19 vaccines: A case study of Thailand

This paper addresses a framework for the operational allocation and administration of COVID-19 vaccines in Thailand, based on both COVID-19 transmission dynamics and other vital operational restrictions that might affect the effectiveness of vaccination strategies in the early stage of vaccine rollout. In this framework, the SIQRV model is first developed and later combined with the COVID-19 Vaccine Allocation Problem (CVAP) to determine the optimal allocation/administration strategies that minimize total weighted strain on the whole healthcare system. According to Thailand's second pandemic wave data (17^th January 2021, to 15^th February 2021), we find that the epicenter-based strategy is surprisingly the worst allocation strategy, due largely to the negligence of provincial demographics, vaccine efficacy, and overall transmission dynamics that lead to higher number of infectious individuals. We also find that early vaccination seems to significantly contribute to the reduction in the number of infectious individuals, whose effects tend to increase with more vaccine supply. With these insights, healthcare policy-makers should therefore focus not only on the procurement of COVID-19 vaccines at strategic levels but also on the allocation and administration of such vaccines at operational levels for the best of their limited vaccine supply.

Modelling and optimal control of multi strain epidemics, with application to COVID-19

Reinfection and multiple viral strains are among the latest challenges in the current COVID-19 pandemic. In contrast, epidemic models often consider a single strain and perennial immunity. To bridge this gap, we present a new epidemic model that simultaneously considers multiple viral strains and reinfection due to waning immunity. The model is general, applies to any viral disease and includes an optimal control formulation to seek a trade-off between the societal and economic costs of mitigation. We validate the model, with and without mitigation, in the light of the COVID-19 epidemic in England and in the state of Amazonas, Brazil. The model can derive optimal mitigation strategies for any number of viral strains, whilst also evaluating the effect of distinct mitigation costs on the infection levels. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, and therefore demand more restrictive measures in the future.

Estimation of exogenous drivers to predict COVID-19 pandemic using a method from nonlinear control theory

The currently ongoing COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. Epidemiological models play a crucial role, thereby assisting policymakers to predict the future course of infections and hospitalizations. One difficulty with current models is the existence of exogenous and unmeasurable variables and their significant effect on the infection dynamics. In this paper, we show how a method from nonlinear control theory can complement common compartmental epidemiological models. As a result, one can estimate and predict these exogenous variables requiring the reported infection cases as the only data source. The method allows to investigate how the estimates of exogenous variables are influenced by non-pharmaceutical interventions and how imminent epidemic waves could already be predicted at an early stage. In this way, the concept can serve as an "epidemometer" and guide the optimal timing of interventions. Analyses of the COVID-19 epidemic in various countries demonstrate the feasibility and potential of the proposed approach. The generic character of the method allows for straightforward extension to different epidemiological models.

The relation between length of lockdown, numbers of infected people and deaths of Covid-19, and economic growth of countries: Lessons learned to cope with future pandemics similar to Covid-19 and to constrain the deterioration of economic system

How is the relation between duration of lockdown and numbers of infected people and deaths of Coronavirus disease 2019 (COVID-19), and growth level of Gross Domestic Product (GDP) in countries? Results here suggest that, during the first wave of COVID-19 pandemic, countries with a shorter period of lockdown (about 15 days: Austria, Portugal and Sweden) have average confirmed cases divided by population higher than countries with a longer period of lockdown (about 60 days, i.e., 2 months: France, Italy and Spain); moreover, countries with a shorter period of lockdown have average fatality rate (5.45%) lower than countries with a longer length of lockdown (12.70%), whereas average variation of fatality rate from March to August 2020 (first pandemic wave of COVID-19) suggests a higher reduction in countries with a longer period of lockdown than countries with a shorter duration (−1.9% vs. −0.72%). Independent Samples Test reveals that average fatality rate of countries with a shorter period of lockdown was significantly lower than countries with a longer period of lockdown (5.4% vs. 12.7%, p-value<.05). The Mann-Whitney Test confirms that average fatality rate of countries with a shorter period of lockdown is significantly lower than countries having a longer period of lockdown (U = 0, p-value = .005). In addition, results show that lockdowns of longer duration have generated negative effects on GDP growth: average contraction of GDP (index 2010 = 100) from second quarter 2019 to second quarter of 2020 in countries applying a longer period of lockdown (i.e., about two months) is about −21%, whereas it is −13% in countries applying a shorter period of lockdown of about 15 days (significant difference with Independent Samples Test: t₄ = −2.274, p-value < .085). This finding shows a systematic deterioration of economic system because of containment policies based on a longer duration of lockdown in society. Another novel finding here reveals that countries with higher investments in healthcare (as percentage of GDP) have alleviated fatality rate of COVID-19 and simultaneously have applied a shorter period of lockdown, reducing negative effects on economic system in terms of contraction of economic growth. Overall, then, using lessons learned of the first wave of COVID-19 pandemic crisis, this study must conclude that a strategy to reduce the negative impact of future epidemics similar to COVID-19 has to be based on a reinforcement of healthcare sector to have efficient health organizations to cope with pandemics of new viral agents by minimizing fatality rates; finally, high investments in health sector create the social conditions to apply lockdowns of short run with lower negative effects on socioeconomic systems.

The Use of GIS Technology to Optimize COVID-19 Vaccine Distribution: A Case Study of the City of Warsaw, Poland

The COVID-19 pandemic is a global challenge, and the key to tackling it is vaccinating a specified percentage of the population to acquire herd immunity. The observed problems with the efficiency of the vaccination campaigns in numerous countries around the world, as well as the approach used at the initial stage of the National Immunization Program in Poland, prompted us to analyse the possibility of using GIS technology to optimize the distribution of vaccines to vaccination sites so as to minimize the period needed to vaccinate individual population groups. The research work was carried out on the example of Warsaw, the capital of Poland and the city with the largest population in the country. The analyses were carried out for the 60-70 and 50-60 age groups, in various approaches and for vaccines of different companies (Moderna, BioNTech, AstraZeneca), used to vaccinate people in Poland. The proposed approach to optimize vaccine distribution uses Thiessen's tessellation to obtain information on the number of people in a given population group living in the area of each vaccination site, and then to estimate the time needed to vaccinate that group. Compared to the originally used vaccination scenario with limited availability of vaccines, the proposed approach allows practitioners to design fast and efficient distribution scenarios. With the developed methodology, we demonstrated ways to achieve uniform vaccination coverage throughout the city. We anticipate that the proposed approach can be easily automated and broadly applied to various urban settings.

Airborne Coronaviruses: Observations from Veterinary Experience

The virus responsible for the pandemic that has affected 152 countries worldwide is a new strain of coronavirus (CoV), which belongs to a family of viruses widespread in many animal species, including birds, and mammals including humans. Indeed, CoVs are known in veterinary medicine affecting several species, and causing respiratory and/or enteric, systemic diseases and reproductive disease in poultry. Animal diseases caused by CoV may be considered from the following different perspectives: livestock and poultry CoVs cause mainly "population disease"; while in companion animals they are a source of mainly "individual/single subject disease". Therefore, respiratory CoV diseases in high-density, large populations of livestock or poultry may be a suitable example for the current SARS-CoV-2/COVID-19 pandemic. In this review we describe some strategies applied in veterinary medicine to control CoV and discuss if they may help to develop practical and useful strategies to control the SARS-CoV-2/COVID-19 pandemic.

Flattening the curves: on-off lock-down strategies for COVID-19 with an application to Brazil

The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s13362-020-00098-w.

Applications of artificial intelligence in battling against covid-19: A literature review

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Assessing the impact of coordinated COVID-19 exit strategies across Europe

As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.

Beyond just "flattening the curve": Optimal control of epidemics with purely non-pharmaceutical interventions

When effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, home quarantine and far-reaching shutdown of public life are the only available strategies to prevent the spread of epidemics. Based on an extended SEIR (susceptible-exposed-infectious-recovered) model and continuous-time optimal control theory, we compute the optimal non-pharmaceutical intervention strategy for the case that a vaccine is never found and complete containment (eradication of the epidemic) is impossible. In this case, the optimal control must meet competing requirements: First, the minimization of disease-related deaths, and, second, the establishment of a sufficient degree of natural immunity at the end of the measures, in order to exclude a second wave. Moreover, the socio-economic costs of the intervention shall be kept at a minimum. The numerically computed optimal control strategy is a single-intervention scenario that goes beyond heuristically motivated interventions and simple "flattening of the curve". Careful analysis of the computed control strategy reveals, however, that the obtained solution is in fact a tightrope walk close to the stability boundary of the system, where socio-economic costs and the risk of a new outbreak must be constantly balanced against one another. The model system is calibrated to reproduce the initial exponential growth phase of the COVID-19 pandemic in Germany.

Beyond just "flattening the curve": Optimal control of epidemics with purely non-pharmaceutical interventions

When effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, home quarantine and far-reaching shutdown of public life are the only available strategies to prevent the spread of epidemics. Based on an extended SEIR (susceptible-exposed-infectious-recovered) model and continuous-time optimal control theory, we compute the optimal non-pharmaceutical intervention strategy for the case that a vaccine is never found and complete containment (eradication of the epidemic) is impossible. In this case, the optimal control must meet competing requirements: First, the minimization of disease-related deaths, and, second, the establishment of a sufficient degree of natural immunity at the end of the measures, in order to exclude a second wave. Moreover, the socio-economic costs of the intervention shall be kept at a minimum. The numerically computed optimal control strategy is a single-intervention scenario that goes beyond heuristically motivated interventions and simple "flattening of the curve". Careful analysis of the computed control strategy reveals, however, that the obtained solution is in fact a tightrope walk close to the stability boundary of the system, where socio-economic costs and the risk of a new outbreak must be constantly balanced against one another. The model system is calibrated to reproduce the initial exponential growth phase of the COVID-19 pandemic in Germany.