To isolate or not to isolate: the impact of changing behavior on COVID-19 transmission

Mathematical analysis of simple behavioral epidemic models

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible-Exposed-Infectious-Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model's ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.

COVID-19 isolation and quarantine orders in Berlin-Reinickendorf (Germany): How many, how long and to whom?

Isolating COVID-19 cases and quarantining their close contacts can prevent COVID-19 transmissions but also inflict harm. We analysed isolation and quarantine orders by the local public health agency in Berlin-Reinickendorf (Germany) and their dependence on the recommendations by the Robert Koch Institute, the national public health institute. Between 3 March 2020 and 18 December 2021 the local public health agency ordered 24 603 isolations (9.2 per 100 inhabitants) and 45 014 quarantines (17 per 100 inhabitants) in a population of 266 123. The mean contacts per case was 1.9. More days of quarantine per 100 inhabitants were ordered for children than for adults: 4.1 for children aged 0-6, 5.2 for children aged 7-17, 0.9 for adults aged 18-64 and 0.3 for senior citizens aged 65-110. The mean duration for isolation orders was 10.2 and for quarantine orders 8.2 days. We calculated a delay of 4 days between contact and quarantine order. 3484 contact persons were in quarantine when they developed an infection. This represents 8% of all individuals in quarantine and 14% of those in isolation. Our study quantifies isolation and quarantine orders, shows that children had been ordered to quarantine more than adults and that there were fewer school days lost to isolation or quarantine as compared to school closures. Our results indicate that the recommendations of the Robert Koch Institute had an influence on isolation and quarantine duration as well as contact identification and that the local public health agency was not able to provide rigorous contact tracing, as the mean number of contacts was lower than the mean number of contacts per person known from literature. Additionally, a considerable portion of the population underwent isolation or quarantine, with a notable number of cases emerging during the quarantine period.

Global lineage evolution pattern of sars-cov-2 in Africa, America, Europe, and Asia: A comparative analysis of variant clusters and their relevance across continents

Objective

The objective of this study is to provide a comparative analysis of variant clusters and their relevance across Africa, America, Europe, and Asia, in order to understand the evolutionary patterns of the virus across different regions and to inform the development of targeted interventions and genomic surveillance eforts.

Methods

The study analyzed the global lineage evolution pattern of 74, 075 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes from 32 countries across four continents, focusing on variant clusters and their relevance across regions. Variants were weighted according to their hierarchical level. The correlation between variants was visualized through Dimensionality reduction analysis and Pairwise Pearson's correlation. We presented a reconstructed phylogenetic tree based on correlation analysis and variant weights.

Results

The analysis revealed that each continent had distinct variant clusters and different evolutionary patterns. The Americas had two clustered variants before lineage divergence and a downstream confluence lineage, Europe had bifurcation into two global lineages with an early occurrence of certain cluster while Asia had a downstream confluence of two large lineages diverging by two distinct clusters. Based on the cluster patterns of shared variants of the SARS-CoV-2 virus, Africa demonstrated a relatively clear distinction among three distinct regions.

Conclusions

The study provides insights into the evolutionary patterns of SARS-CoV-2 and highlights the importance of international collaboration in tracking and responding to emerging variants. The study found that the global pandemic was driven by Omicron variants that evolved with significant differences between countries and regions, and with different patterns across continents.

Geoepidemiological perspective on COVID-19 pandemic review, an insight into the global impact

The COVID-19 pandemic showed major impacts, on societies worldwide, challenging healthcare systems, economies, and daily life of people. Geoepidemiology, an emerging field that combines geography and epidemiology, has played a vital role in understanding and combatting the spread of the virus. This interdisciplinary approach has provided insights into the spatial patterns, risk factors, and transmission dynamics of the COVID-19 pandemic at different scales, from local communities to global populations. Spatial patterns have revealed variations in incidence rates, with urban-rural divides and regional hotspots playing significant roles. Cross-border transmission has highlighted the importance of travel restrictions and coordinated public health responses. Risk factors such as age, underlying health conditions, socioeconomic factors, occupation, demographics, and behavior have influenced vulnerability and outcomes. Geoepidemiology has also provided insights into the transmissibility and spread of COVID-19, emphasizing the importance of asymptomatic and pre-symptomatic transmission, super-spreading events, and the impact of variants. Geoepidemiology should be vital in understanding and responding to evolving new viral challenges of this and future pandemics.

Time delay of the appearance of a new strain can affect vaccination behavior and disease dynamics: An evolutionary explanation

The emergence of a novel strain during a pandemic, like the current COVID-19, is a major concern to the healthcare system. The most effective strategy to control this type of pandemic is vaccination. Many previous studies suggest that the existing vaccine may not be fully effective against the new strain. Additionally, the new strain's late arrival has a significant impact on the disease dynamics and vaccine coverage. Focusing on these issues, this study presents a two-strain epidemic model in which the new strain appears with a time delay. We considered two vaccination provisions, namely preinfection and postinfection vaccinations, which are governed by human behavioral dynamics. In such a framework, individuals have the option to commit vaccination before being infected with the first strain. Additionally, people who forgo vaccination and become infected with the first train have the chance to be vaccinated (after recovery) in an attempt to avoid infection from the second strain. However, a second strain can infect vaccinated and unvaccinated individuals. People may have additional opportunities to be vaccinated and to protect themselves from the second strain due to the time delay. Considering the cost of the vaccine, the severity of the new strain, and the vaccine's effectiveness, our results indicated that delaying the second strain decreases the peak size of the infected individuals. Finally, by estimating the social efficiency deficit, we discovered that the social dilemma for receiving immunization decreases with the delay in the arrival of the second strain.

A mathematical model for the co-dynamics of COVID-19 and tuberculosis

In this study, we formulated and analyzed a deterministic mathematical model for the co-infection of COVID-19 and tuberculosis, to study the co-dynamics and impact of each disease in a given population. Using each disease's corresponding reproduction number, the existence and stability of the disease-free equilibrium were established. When the respective threshold quantitiesR C , andR T are below unity, the COVID-19 and TB-free equilibrium are said to be locally asymptotically stable. The impact of vaccine (i.e., efficacy and vaccinated proportion) and the condition required for COVID-19 eradication was examined. Furthermore, the presence of the endemic equilibria of the sub-models is analyzed and the criteria for the phenomenon of backward bifurcation of the COVID-19 sub-model are presented. To better understand how each disease condition impacts the dynamics behavior of the other, we investigate the invasion criterion of each disease by computing the threshold quantity known as the invasion reproduction number. We perform a numerical simulation to investigate the impact of threshold quantities ( R C , R T ) with respect to their invasion reproduction number, co-infection transmission rate ( β c t ) , and each disease transmission rate ( β c , β t ) on disease dynamics. The outcomes established the necessity for the coexistence or elimination of both diseases from the communities. Overall, our findings imply that while COVID-19 incidence decreases with co-infection prevalence, the burden of tuberculosis on the human population increases.

The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps

Despite the availability of effective vaccines, the persistence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) suggests that cocirculation with other pathogens and resulting multiepidemics (of, for example, COVID-19 and influenza) may become increasingly frequent. To better forecast and control the risk of such multiepidemics, it is essential to elucidate the potential interactions of SARS-CoV-2 with other pathogens; these interactions, however, remain poorly defined. Here, we aimed to review the current body of evidence about SARS-CoV-2 interactions. Our review is structured in four parts. To study pathogen interactions in a systematic and comprehensive way, we first developed a general framework to capture their major components: sign (either negative for antagonistic interactions or positive for synergistic interactions), strength (i.e., magnitude of the interaction), symmetry (describing whether the interaction depends on the order of infection of interacting pathogens), duration (describing whether the interaction is short-lived or long-lived), and mechanism (e.g., whether interaction modifies susceptibility to infection, transmissibility of infection, or severity of disease). Second, we reviewed the experimental evidence from animal models about SARS-CoV-2 interactions. Of the 14 studies identified, 11 focused on the outcomes of coinfection with nonattenuated influenza A viruses (IAVs), and 3 with other pathogens. The 11 studies on IAV used different designs and animal models (ferrets, hamsters, and mice) but generally demonstrated that coinfection increased disease severity compared with either monoinfection. By contrast, the effect of coinfection on the viral load of either virus was variable and inconsistent across studies. Third, we reviewed the epidemiological evidence about SARS-CoV-2 interactions in human populations. Although numerous studies were identified, only a few were specifically designed to infer interaction, and many were prone to multiple biases, including confounding. Nevertheless, their results suggested that influenza and pneumococcal conjugate vaccinations were associated with a reduced risk of SARS-CoV-2 infection. Finally, fourth, we formulated simple transmission models of SARS-CoV-2 cocirculation with an epidemic viral pathogen or an endemic bacterial pathogen, showing how they can naturally incorporate the proposed framework. More generally, we argue that such models, when designed with an integrative and multidisciplinary perspective, will be invaluable tools to resolve the substantial uncertainties that remain about SARS-CoV-2 interactions.

Impact of public sentiments on the transmission of COVID-19 across a geographical gradient

COVID-19 is a respiratory disease caused by a recently discovered, novel coronavirus, SARS-COV-2. The disease has led to over 81 million confirmed cases of COVID-19, with close to two million deaths. In the current social climate, the risk of COVID-19 infection is driven by individual and public perception of risk and sentiments. A number of factors influences public perception, including an individual's belief system, prior knowledge about a disease and information about a disease. In this article, we develop a model for COVID-19 using a system of ordinary differential equations following the natural history of the infection. The model uniquely incorporates social behavioral aspects such as quarantine and quarantine violation. The model is further driven by people's sentiments (positive and negative) which accounts for the influence of disinformation. People's sentiments were obtained by parsing through and analyzing COVID-19 related tweets from Twitter, a social media platform across six countries. Our results show that our model incorporating public sentiments is able to capture the trend in the trajectory of the epidemic curve of the reported cases. Furthermore, our results show that positive public sentiments reduce disease burden in the community. Our results also show that quarantine violation and early discharge of the infected population amplifies the disease burden on the community. Hence, it is important to account for public sentiment and individual social behavior in epidemic models developed to study diseases like COVID-19.

We are stronger and better together

I learned during twenty years in RECOOP HST Association that sharing ideas, plans and actions make a human network naturally productive. Also, I believed throughout these years that honesty is a key component of a healthy relationship. Not only because honesty helps us avoid harmful breaches of trust, but because it allows us to build creative collaborative research organizations. Nonetheless, in the last two years, we were challenged with a vast scale of crises. First, the COVID-19 pandemic and second, beginning February 24, 2022, the brutal Russian aggression against Ukraine have impacted everybody’s life causing significant concern and insecurity across the globe. Economic instability, disturbed work routines and disrupted family lives have caused higher risks of physical and mental disorders [1, 2]. Socially isolated people often feel lonely or depressed. Constantly lonely people have higher blood pressure, are more vulnerable to infection and are more likely to develop Alzheimer’s disease and dementia. Loneliness also interferes with an entire range of everyday functions such as sleep patterns, attention and logical and verbal reasoning [3-6]. At the time the pandemic calmed, the Russian barbaric aggression and bombardment of civilian targets exposed large populations to physical aggression, which grants more damage over time as many Ukrainians are losing human condition. Are we losing humanity also? Humans are good or bad, or predators also capable of great kindness. Humanity symbolizes human love and sympathy toward each other. Human qualities include honesty, integrity, courage, self-awareness and dedication. These qualities define who we are as human beings. In this troubled time, we must look for the good in people and strengthen the care for others. Gandhi taught us, “The power of humanity is the strength of individual commitment and the force of collective action.” When RECOOP HST Association does something to help in Ukraine, all of us gain a sense of being valuable, helpful and worthwhile. Gandhi’s words remind us, “The best way to find yourself is to lose yourself in the service of others.” The human misery caused by Russia’s attacks only strengthen Ukrainians’ extraordinary spirit and their dedication to protect their family, culture and homeland. Putin is scrambled by the strength of Ukraine and getting more aggressive. His actions prove, like Gandhi wrote, “The weak can never forgive. Forgiveness is the attribute of the strong.” And we are stronger and better together. Keywords: Alzheimer’s disease, blood pressure, COVID-19, dementia, pandemic, physical and mental disorders, research organizations

Nonlinear optimal control strategies for a mathematical model of COVID-19 and influenza co-infection

Infectious diseases have remained one of humanity's biggest problems for decades. Multiple disease infections, in particular, have been shown to increase the difficulty of diagnosing and treating infected people, resulting in worsening human health. For example, the presence of influenza in the population is exacerbating the ongoing COVID-19 pandemic. We formulate and analyze a deterministic mathematical model that incorporates the biological dynamics of COVID-19 and influenza to effectively investigate the co-dynamics of the two diseases in the public. The existence and stability of the disease-free equilibrium of COVID-19-only and influenza-only sub-models are established by using their respective threshold quantities. The result shows that the COVID-19 free equilibrium is locally asymptotically stable whenR C < 1 , whereas the influenza-only model, is locally asymptotically stable whenR F < 1 . Furthermore, the existence of the endemic equilibria of the sub-models is examined while the conditions for the phenomenon of backward bifurcation are presented. A generalized analytical result of the COVID-19-influenza co-infection model is presented. We run a numerical simulation on the model without optimal control to see how competitive outcomes between-hosts and within-hosts affect disease co-dynamics. The findings established that disease competitive dynamics in the population are determined by transmission probabilities and threshold quantities. To obtain the optimal control problem, we extend the formulated model by including three time-dependent control functions. The maximum principle of Pontryagin was used to prove the existence of the optimal control problem and to derive the necessary conditions for optimum disease control. A numerical simulation was performed to demonstrate the impact of different combinations of control strategies on the infected population. The findings show that, while single and twofold control interventions can be used to reduce disease, the threefold control intervention, which incorporates all three controls, will be the most effective in reducing COVID-19 and influenza in the population.

A game-theoretic model of rabies in domestic dogs with multiple voluntary preventive measures

Game theory is now routinely applied to quantitatively model the decision making of individuals presented with various voluntary actions that can prevent a given disease. Most models consider only a single preventive strategy and the case where multiple preventative actions are available is severely understudied. In our paper, we consider a very simple SI compartmental model of rabies in the domestic dog population. We study two choices of the dog owners: to vaccinate their dogs or to restrict the movements of unvaccinated dogs. We analyze the relatively rich patterns of Nash equilibria (NE). We show that there is always at least one NE at which the owners utilize only one form of prevention. However, there can be up to three different NEs at the same time: two NEs at which the owners use exclusively only the vaccination or movement restriction, and the third NE when the owners use both forms of prevention simultaneously. However, we also show that, unlike the first two types of NEs, the third kind of NE is not convergent stable.

A game-theoretic model of lymphatic filariasis prevention

Lymphatic filariasis (LF) is a mosquito-borne parasitic neglected tropical disease. In 2000, WHO launched the Global Programme to Eliminate Lymphatic Filariasis (GPELF) as a public health problem. In 2020, new goals for 2030 were set which includes a reduction to 0 of the total population requiring Mass Drug Administrations (MDA), a primary tool of GPELF. We develop a mathematical model to study what can happen at the end of MDA. We use a game-theoretic approach to assess the voluntary use of insect repellents in the prevention of the spread of LF through vector bites. Our results show that when individuals use what they perceive as optimal levels of protection, the LF incidence rates will become high. This is in striking difference to other vector-borne NTDs such as Chagas or zika. We conclude that the voluntary use of the protection alone will not be enough to keep LF eliminated as a public health problem and a more coordinated effort will be needed at the end of MDA.

The Impact of Testing Capacity and Compliance With Isolation on COVID-19: A Mathematical Modeling Study

Introduction

Diagnostic tests can play an important role in reducing the transmission of infectious respiratory diseases, particularly during a pandemic. The potential benefit of diagnostic testing depends on at least 4 factors: (1) how soon testing becomes available after the beginning of the pandemic and (2) at what capacity; (3) compliance with isolation after testing positive; and (4) compliance with isolation when experiencing symptoms, even in the absence of testing.

Methods

To understand the interplay between these factors and provide further insight into policy decisions for future pandemics, we developed a compartmental model and simulated numerous scenarios using the dynamics of COVID-19 as a case study.

Results

Our results quantified the significant benefits of early start of testing and high compliance with isolation. Early start of testing, even with low testing capacity over time, could significantly slow down the disease spread if compliance with isolation is high. By contrast, when the start of testing was delayed, the benefit of testing on reducing infection spread was limited, even when testing capacity was high; the additional testing capacity required increased superlinearly for each day of delay to achieve a similar infection attack rate as in starting testing earlier.

Conclusions

Our study highlighted the importance of the early start of testing and public health messaging to promote isolation compliance when needed for an ongoing effective response to COVID-19 and future pandemics.

Impact of COVID-19 on Health Seeking Behavior of Patients with Chronic Disease at Public Hospitals in Jimma Zone, South West Ethiopia

Background

COVID-19 is a global pandemic with unprecedented medical, economic and social consequences affecting nations across the world. This epidemic arises while chronic diseases are continued to be a public health concern. Though evidence is generated on its impact on the health care system, little is known about the Impact of COVID −19 on the care-seeking behavior of chronic patients.

Objective

To assess the Impact of COVID-19 on healthcare-seeking behavior of patients with chronic diseases attending follow-up at public hospitals in Jimma zone, South West Ethiopia.

Methods

Facility-based cross-sectional study design was employed. The sample was calculated using the single population proportion formula. Hospitals were selected by using simple random sampling. Then, the final calculated sample size for the study was proportionally allocated to each selected hospital. Data were collected from 400 participants through face-to-face interviews and card reviews. Data were entered into Epi-Data version 3.1 and then exported to SPSS version 23 for analysis. Binary and multivariable logistic regression analyses with 95% CI for odds ratio (OR) were used to identify significant factors.

Results

Of the total respondents 156 (39.0%) of them had poor health-seeking behavior. Contact history with COVID −19 patient (AOR = 2.8; 95% CI = 1.1–7.0), perceived moderate depression (AOR = 2.3; 95% CI = 1.2–4.2), perceived extreme depression (AOR = 4.3; 95% CI = 1.8–10.5), shortage of medication (AOR = 2.4; 95% CI = 1.0–6.2) increases the odds of poor health-seeking behavior. In addition, the odds of poor health-seeking in patients with no formal education were higher compared to patients with higher educational status (AOR = 2.7; 95% CI = 1.0–9.0).

Conclusion

COVID −19 outbreaks affected the health-seeking behavior of patients with chronic diseases. The impact was found to be more significant among patients who had a contact history with COVID −19 patients. Moreover, perceived depression, shortage of medication, and low educational status were significant predictors of poor health-seeking behavior. Therefore, working on the barriers to the health-seeking behavior of chronic patients may reduce the effect of COVID-19.