Total Number Of Infections Research Articles

Seroprevalence of anti-SARS-CoV-2 antibodies in Indore, Madhya Pradesh: A community-based cross-sectional study, August 2020.

Background:In India, laboratory diagnosis of SARS – CoV-2 infection has been mostly based on real-time reverse transcriptase-polymerase chain reaction (RT-PCR). Studies have shown that Viral titres peak within the first week of symptoms but may decline later hampering RT-PCR-based diagnostic strategies. Exact estimate is difficult under high-risk screening strategy with evidences of having large number of asymptomatic cases. This has prompted a call for adoption of antibody testing as potential source of data.Materials and Methods:A cross-sectional study with a sample size of 7000 was conducted for 15 days including all the 85 wards under Indore Municipal Corporation. Stratified Random Sampling was used to collect the samples. Trained teams collected basic sociodemographic information and serum samples which were tested for the presence of specific antibodies to COVID-19 using ICMR-Kavach IgG ELISA kits. The data collected was compiled and analysed using appropriate statistical software.Results:Overall weighted seroprevalence of the study population was found to be 7.75%. The prevalence in males and females was comparable (7.91% vs 7.57%). Highest seropositivity (10.04%) was seen among individuals aged more than 60 years. Total number of infections in the population were estimated to be 2,03,160. Overall Case Infection Ratio was found to be 27.43.Conclusion:The current seroprevalence study provides information on proportion of the population exposed, but the correlation between presence and absence of antibodies is not a marker of total or partial immunity. It must also be noted that more than 90 percent of the population is still susceptible for COVID-19 infection. Hence, non-pharmaceutical interventions like respiratory hygiene, physical distancing, hand sanitization, usage of personal protective equipment such as masks and implementation of public health measures need to be continued.

Fragile Strategies: Getting Pandemic Response Right in Fragile States

Fragile Strategies: Getting Pandemic Response Right in Fragile States1 Blaise Misztal (bio) This article was contributed to Forum—the edition’s portfolio of thematic content—by GJIA’s Global Governance section Crises, conventional wisdom holds, are the only way to jolt our divided, distracted, and dawdling political system into action. In the case of the COVID-19 pandemic and its impact on the world’s fragile states, however, policy responses prompted by the current crisis might actually prove counterproductive. Almost as soon as it became clear in the spring of 2020 that the rapid spread of the novel coronavirus was becoming a global pandemic, alarm bells began ringing among experts who study and work in fragile contexts. Fragility is a political condition best understood as affecting low- or middle-income states—or sub-state regions—that are, or are at risk of becoming, engulfed in political instability or conflict as a result of “deficits of institutional capacity and political legitimacy.”2 The World Bank currently includes thirty-six areas in its list of “Fragile and Conflict-affected Situations.”3 Yet, according to available data, fragile states were recording far fewer COVID-19 cases and deaths than other developing but non-fragile countries, let alone the developed world. This does not mean that the experts were wrong or that the COVID-19 pandemic is not ravaging these countries. Indeed, regardless of its effects on public health—which might be far greater than limited testing data reveals—COVID-19’s second-order economic, social, and political consequences are already severe and threaten to be long-lasting for fragile states. However, these early concerns about fragile states—and the resulting donor response focused on broad health and humanitarian assistance— contradict most of what is known about fragility and how to deal with it. In the last several years, a consensus has emerged in United States and across multilateral institutions—within governmental and non-governmental organizations alike and now captured in recent US legislation and official strategy documents4—on how to address the challenge of fragility. This approach focuses upon individually tailored, long-term preventive engagements designed around building political comity, not through crisis-driven humanitarian assistance or technical capacity building. Yet, these lessons are being sparingly applied—if at all—to the challenge that COVID-19 poses to fragile states. In short, COVID-19 risks not only decimating fragile states but also undoing the progress that has been made in rethinking policy responses to fragility. An unpredictable pandemic In the spring of 2020 and as Europe and the United States were in various stages of lockdown, David Miliband, the former British Foreign Minister and current head of the International Rescue Committee (IRC), warned that “the full, devastating and disproportionate weight of this pandemic has yet to be felt in the world’s most fragile and war-torn countries.”5 The IRC estimated that there could be “between 500 million and 1 billion coronavirus infections, leading to between 1.7 to 3.2 million deaths” in the thirty-four predominantly fragile countries within which the agency works.6 Available data shows that—thus far—this worst-case scenario has not come to pass. As of [End Page 29] January 29, 2021, total worldwide reported COVID-19 infections stood at roughly 102 million, with deaths at 2.2 million. Though there are concerns about the accuracy of that data, fragile states have reported far lower infection and mortality rates than other developing and even developed countries. In the thirty-six areas categorized as fragile by the World Bank, the total number of infections (as of early December 2020) was 1.25 million—with just over 27,000 deaths. Taken together, fragile states average 227 total infections and five total deaths per 100,000 inhabitants, rates that are an order of magnitude lower than the United States—where those figures stand at 4,638 and 88, respectively.7 Even more remarkable is that fragile states in Africa (twenty out of the thirty-six states designated as fragile by the World Bank)—and indeed the continent as a whole—have fared much better than expected. In June, the head of Africa Centers for Disease...

Survey of Smartphone Contact-Tracing Apps Architectures

The outbreak of COVID-19 has caused a cataclysmic impact on the world. The highly infectious virus is known to spread rapidly where the person exposed does not exhibit the symptoms initially. The respondent governments are using contact tracing apps to help in the automation of the process to trace the recent contacts of the infected person. This is a survey to review and examine various smartphone contact tracing App architectures and Technologies used to connect the smartphones. So as to compare them to find the best solution to the problem. We have analyzed a few research papers and their results are compared. We have seen that many studies found that contact tracing could stop an outbreak by reducing the reproduction rate and thus reducing the total number of infections.

A simulation–optimization framework for optimizing response strategies to epidemics

Epidemics require dynamic response strategies that encompass a multitude of policy alternatives and that balance health, economic and societal considerations. We propose a simulation–optimization framework to aid policymakers select closure, protection and travel policies to minimize the total number of infections under a limited budget. The proposed framework combines a modified, age-stratified SEIR compartmental model to evaluate the health impact of response strategies and a Genetic Algorithm to effectively search for better strategies. We implemented our framework on a real case study in Nova Scotia to devise optimized response strategies to COVID-19 under different budget scenarios and found a clear trade-off between health and economic considerations. Closure policies seem to be the most sensitive to policy restrictions, followed by travel policies. On the other hand, results suggest that practising social distancing and wearing masks are necessary whenever their economic impacts are bearable. The framework is generic and can be extended to encompass vaccination policies and to use different epidemiological models and optimization methods.

A focus on the spread of the delta variant of SARS-CoV-2 in India.

A focus on the spread of the delta variant of SARS-CoV-2 in India.

A Multi-Stage Stochastic Programming Approach to Epidemic Resource Allocation with Equity Considerations

Existing compartmental models in epidemiology are limited in terms of optimizing the resource allocation to control an epidemic outbreak under disease growth uncertainty. In this study, we address this core limitation by presenting a multi-stage stochastic programming compartmental model, which integrates the uncertain disease progression and resource allocation to control an infectious disease outbreak. The proposed multi-stage stochastic program involves various disease growth scenarios and optimizes the distribution of treatment centers and resources while minimizing the total expected number of new infections and funerals. We define two new equity metrics, namely infection and capacity equity, and explicitly consider equity for allocating treatment funds and facilities over multiple time stages. We also study the multi-stage value of the stochastic solution (VSS), which demonstrates the superiority of the proposed stochastic programming model over its deterministic counterpart. We apply the proposed formulation to control the Ebola Virus Disease (EVD) in Guinea, Sierra Leone, and Liberia of West Africa to determine the optimal and fair resource-allocation strategies. Our model balances the proportion of infections over all regions, even without including the infection equity or prevalence equity constraints. Model results also show that allocating treatment resources proportional to population is sub-optimal, and enforcing such a resource allocation policy might adversely impact the total number of infections and deaths, and thus resulting in a high cost that we have to pay for the fairness. Our multi-stage stochastic epidemic-logistics model is practical and can be adapted to control other infectious diseases in meta-populations and dynamically evolving situations.

The SEIR model of COVID-19 forecasting rates of infections in New York City

We used SEIR mathematical models to simulate the spread of COVID-19 in New York City, selecting a period of time to estimate the total number of infections and to predict the date of the highest number of infections. We found that the cumulative number of people infected or infected and the proportion of those infected increased with the increase in infectivity; The peak of infectious diseases is brought forward with the decrease of infectiousness; The number of people, Ir + Iu (prevalence), and the estimated peak of the disease occurred on April 25. At R_0, the regeneration number is much higher than 1, which is proportional to the infection rate. Since the infectiousness of unreported cases is not known, we can predict the outbreak under three different scenarios and get reasonable results.

THE SCALE OF THE SPREAD OF COVID-19 IN GEORGIA AND EFFECTIVENESS OF PREVENTIVE MEASURES IMPLIMENTED BY THE GOVERNMENT – WHAT DO MODELS SAY?

. In this article we present forecasts of the spread of COVID-19 virus, obtained by econometric and machine learning methods. Furthermore, by employing modelling method, we estimate effectiveness of preventive measures implemented by the government. Each of the models discussed in this article is modelling different characteristics of the COVID-19 epidemic’s trajectory: peak and end date, number of daily infections over different forecasting horizons, total number of infection cases. All these provide quite clear picture to the interested reader of the future threats posed by COVID-19. In terms of existing models and data, our research indicates that phenomenological models do well in forecasting the trend, duration and total infections of the COVID- 19 epidemic, but make serious mistakes in forecasting the number of daily infections. Machine learning models, deliver more accurate short –term forecast of daily infections, but due to data limitations, they struggle to make long-term forecasts. Compartmental models are the best choice for modelling the measures implemented by the government for preventing the spread of COVID-19 and determining optimal level of restrictions. These models show that until achieving herd immunity (i.e. without any epidemiological or government implemented measures), approximate number of people infected with COVID-19 would be 3 million, but due to preventive measures, expected total number of infections has reduced to several thousand (1555-3189) people. This unequivocally indicates the effectiveness of the preventive measures.

PANDEMIC IMPACTS ON THE INTERNATIONAL ECONOMY

In the history of world development, side to formation of the economic and financial systems, temporary crashes are not new. At different times, crises have always been experienced in different countries. However, within the rise of globalization and economic interdependence, modern crises are gaining much huge effects on social and economic systems. More than half a year passed since the novel Coronavirus declared as a pandemic and virus already killed more than 900 000 people, while bringing the total number of infections to more than 28 million. Aside from widen spread of the virus, it can already be boldly said that virus has become the biggest challenge of modern reality since the World War II. The Coronomic crisis, which became actual shortly since the outbreak of COVID-19 virus pandemic, is characterized by a different anatomy due to its nature. Unlike the previous crises, which experienced well-established financial and economic basis, the current crisis is significantly different. This time, the cause the crisis haven›t financial of economic source, but the roots come from medicine. The current situation makes the economy a hostage of medicine (Papava, 2020) which is a dramatically different phenomenon from the previous crashes. Unlike the economic crises over the last decade and century, the Coronomic crisis affects many sectors of the economy. While it puts into the agenda necessity of rethinking existing policy on international trade, production chain and generally structure of the economy. This requires in depth research, which is the aim of this article. Without vaccine and effective clinical treatments agains the virus, social distancing remains the only recommended tool agains the virus. The latter is a significant challenge for the modern economy. The current situation, in addition to the necessity of rethink economic policy also puts into the agenda the issue of establishing new trends in the international trade and economy. The reality in the post-pandemic period, which may lead to changes in the international supply chain and a new dealing out of the supply market, will lead to significant changes in the international economy that requires in-depth research and analysis. The aim of the paper is to analyze some of the above issues, which will be presented on the based of existing empirical data.

Will an outbreak exceed available resources for control? Estimating the risk from invading pathogens using practical definitions of a severe epidemic.

Forecasting whether or not initial reports of disease will be followed by a severe epidemic is an important component of disease management. Standard epidemic risk estimates involve assuming that infections occur according to a branching process and correspond to the probability that the outbreak persists beyond the initial stochastic phase. However, an alternative assessment is to predict whether or not initial cases will lead to a severe epidemic in which available control resources are exceeded. We show how this risk can be estimated by considering three practically relevant potential definitions of a severe epidemic; namely, an outbreak in which: (i) a large number of hosts are infected simultaneously; (ii) a large total number of infections occur; and (iii) the pathogen remains in the population for a long period. We show that the probability of a severe epidemic under these definitions often coincides with the standard branching process estimate for the major epidemic probability. However, these practically relevant risk assessments can also be different from the major epidemic probability, as well as from each other. This holds in different epidemiological systems, highlighting that careful consideration of how to classify a severe epidemic is vital for accurate epidemic risk quantification.

After the honeymoon, the divorce: Unexpected outcomes of disease control measures against endemic infections.

The lack of effective vaccines for many endemic diseases often forces policymakers to rely on non-immunizing control measures, such as vector control, to reduce the massive burden of these diseases. Controls can have well-known counterintuitive effects on endemic infections, including the honeymoon effect, in which partially effective controls cause not only a greater initial reduction in infection than expected, but also large outbreaks during control resulting from accumulation of susceptibles. Unfortunately, many control measures cannot be maintained indefinitely, and the results of cessation are poorly understood. Here, we examine the results of stopped or failed non-immunizing control measures in endemic settings. By using a mathematical model to compare the cumulative number of cases expected with and without control, we show that deployment of control can lead to a larger total number of infections, counting from the time that control started, than without any control–the divorce effect. This result is directly related to the population-level loss of immunity resulting from non-immunizing controls and is seen in a variety of models when non-immunizing controls are used against an infection that confers immunity. Finally, we examine three control plans for minimizing the magnitude of the divorce effect in seasonal infections and show that they are incapable of eliminating the divorce effect. While we do not suggest stopping control programs that rely on non-immunizing controls, our results strongly argue that the accumulation of susceptibility should be considered before deploying such controls against endemic infections when indefinite use of the control is unlikely. We highlight that our results are particularly germane to endemic mosquito-borne infections, such as dengue virus, both for routine management involving vector control and for field trials of novel control approaches, and in the context of non-pharmaceutical interventions aimed at COVID-19.

Abstract PO-024: Incidence of SARS-COV-2 infection in cancer patients undergoing active treatment

Abstract Introduction and Objectives: Cancer patients are more susceptible to infections because of the active treatment that they need to treat their disease. A new coronavirus, called SARS-CoV-2, has caused a global pandemic where cancer patients have an increased risk of morbidity and mortality associated with COVID-19. However, the incidence data of COVID-19 in cancer patient with active treatment are not known, although the main oncology societies recommend a delay and/or stop in active cancer treatment during this pandemic. Whether this stop will have an impact on the future evolution of their disease is also not known. Therefore, a study of the incidence of COVID-19 in this type of patient can help us to organize the protocols and the treatment in these high-risk patient group. Materials and Methods: We conducted a prospective clinical study of cancer patients within active treatment (chemotherapy, palliative hormonotherapy, radiotherapy, target therapies, or immunotherapy), analyzing the number of COVID-19 diagnoses between February 26 and May 13 in two oncology services of the Andalusian community. A descriptive analysis of 692 patients with active treatment was carried out. In addition, the cumulative incidence and the differences between groups were calculated using the SPSS vs 18. Results: A total of 692 cancer patients undergoing active treatment at the Hospital Costa del Sol (Marbella) and at the Hospital San Cecilio (Granada) were analyzed. Sixty four percent were men with a mean age of 60 years. Forty one percent had a breast cancer diagnosis, 12.9% had lung cancer, and 14.5% had colorectal tumor. Fifty three percent of them received treatment for stage IV disease, and up to 43% were delayed treatment due to pandemic. The total number of infections was 9, a cumulative incidence of 1.3%, 95% CI (0.384-2.217), and 22% of them died after developing the infection. Advanced age (p = 0.011), an admission in the 3 months prior to the diagnosis of COVID-19 (p = 0.031), and active treatment with chemotherapy (p = 0.003) were the factors that were associated with an increased risk of developing COVID-19. Conclusions: The incidence of SARS-CoV-2 in cancer patients on active treatment is low but the mortality is high, as previously reported for these patients. Given that the incidence of COVID-19 in patients with treatment is low, we cannot conclude any role of treatment delay in the development of COVID-19 in these patients. Citation Format: Celia Martin Bravo, Isabel Blancas López-Barajas, Raúl Quirós López, Marta Robles Lasarte, Rosa Villatoro Roldán, Julia Alcaide García, Victor Navarro Pérez, Diego Perez Martín, Irene Zarcos Pedrinaci, Elisa Mañas Mora, Nicolás Jiménez García, Santiago Vico Cabra, Irati de Urrutia Undabarrena, Isabel Barragan Mallofret, Cristina Quero Blanco, Elisabeth Pérez-Ruiz. Incidence of SARS-COV-2 infection in cancer patients undergoing active treatment [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-024.

Simulating phase transitions and control measures for network epidemics caused by infections with presymptomatic, asymptomatic, and symptomatic stages.

We investigate phase transitions associated with three control methods for epidemics on small world networks. Motivated by the behavior of SARS-CoV-2, we construct a theoretical SIR model of a virus that exhibits presymptomatic, asymptomatic, and symptomatic stages in two possible pathways. Using agent-based simulations on small world networks, we observe phase transitions for epidemic spread related to: 1) Global social distancing with a fixed probability of adherence. 2) Individually initiated social isolation when a threshold number of contacts are infected. 3) Viral shedding rate. The primary driver of total number of infections is the viral shedding rate, with probability of social distancing being the next critical factor. Individually initiated social isolation was effective when initiated in response to a single infected contact. For each of these control measures, the total number of infections exhibits a sharp phase transition as the strength of the measure is varied.

Results of a national web-based survey on the SARS-CoV-2 infectious state of otorhinolaryngologists in Germany.

PurposeSARS-CoV-2 is detected on the mucosa of the upper airways to a high degree. In the course of the COVID-19 pandemic, otorhinolaryngologists (ORL) are assumed to be at high risk due to close contact with the mucosa of the upper airways. No data are yet available providing evidence that ORLs have an increased risk of infection. MethodsGerman ORLs were invited via e-mail through the German Society of ORL, Head and Neck Surgery and the German ENT Association to participate in a web-based survey about infection with SARS-CoV-2 and development of COVID-19. Data of infections and concomitant parameters in German ORLs were collected and compared to the total number of infections in Germany. ResultsOut of 6383 German ORLs, 970 (15%) participated. 54 ORLs reported testing positive for SARS-CoV-2. Compared to the total population of Germany, ORLs have a relative risk of 3.67 (95% CI 2.82; 4.79) of contracting SARS-CoV-2. Domestic quarantine was conducted in 96.3% of cases. Two individuals were admitted to hospital without intensive care. No casualties were reported. In 31 cases, the source of infection was not identifiable whereas 23 had a clear medical aetiology: infected patients: n = 5, 9.26%; medical staff: n = 13, 14.1%. 9.26% (n = 5) of the identified cases were related to contact to infected family members (n = 3), closer neighbourhood (n = 1) or general public (n = 1). There was no identified increased risk of infection due to performing surgery. ConclusionGerman ORLs have an almost 3.7-fold risk of contracting SARS-CoV-2 compared to the population baseline level. Appropriate protection appears to be necessary for this occupational group.

#FlattenTheCurve

AbstractIn this paper I adapt a common model used in economics to study the diffusion of innovations to model the transmission of a virus. Emphasis is placed on the evolution of the number of new infections and the cumulative total number of infections over time and how they might be influenced by different policies. Although the model is very simple it does yield some useful implications for public policy.

The introduction of population migration to SEIAR for COVID-19 epidemic modeling with an efficient intervention strategy

In this paper, we present a mathematical model of an infectious disease according to the characteristics of the COVID-19 pandemic. The proposed enhanced model, which will be referred to as the SEIR (Susceptible–Exposed–Infectious–Recovered) model with population migration, is inspired by the role that asymptomatic infected individuals, as well as population movements can play a crucial role in spreading the virus. In the model, the infected and the basic reproduction numbers are compared under the influence of intervention policies. The experimental simulation results show the impact of social distancing and migration-in rates on reducing the total number of infections and the basic reproductions. And then, the importance of controlling the number of migration-in people and the policy of restricting residents’ movements in preventing the spread of COVID-19 pandemic are verified.

The COVID-19 pandemic: growth patterns, power law scaling, and saturation

More and more countries are showing a significant slowdown in the number of new COVID-19 infections due to effective governmentally instituted lockdown and social distancing measures. We have analyzed the growth behavior of the top 25 most affected countries by means of a local slope analysis and found three distinct patterns that individual countries follow depending on the strictness of the lockdown protocols: rise and fall, power law, or logistic. For countries showing power law growth we have determined the scaling exponents. For countries that showed a strong slowdown in the rate of infections we have extrapolated the expected saturation of the total number of infections and the expected final date. Three different extrapolation methods (logistic, parabolic, and cutoff power law) were used. All methods agree on the order of magnitude of saturation and end dates. Global infection rates are analyzed with the same methods. The relevance and accuracy of these extrapolations is discussed.

Simplified model of Covid-19 epidemic prognosis under quarantine and estimation of quarantine effectiveness.

A simplified model of Covid-19 epidemic dynamics under quarantine conditions and method to estimate quarantine effectiveness are developed. The model is based on the daily growth rate of new infections when total number of infections is significantly smaller than population size of infected country or region. The model is developed on the basis of collected epidemiological data of Covid19 pandemic, which shows that the daily growth rate of new infections has tendency to decrease linearly when the quarantine is imposed in a country (or a region) until it reaches a constant value, which corresponds to the effectiveness of quarantine measures taken in the country. The daily growth rate of new infections can be used as criteria to estimate quarantine effectiveness.

Optimal quarantine control of an infectious outbreak.

This paper studies the optimal control of an infectious spread based on common epidemic models with permanent immunity and no vaccine availability. Assuming limited isolation control and capacity constraints on the number of infections, an optimal quarantine control strategy that balances between the total number of infections and the overall isolation effort is derived from necessary optimality conditions. The specific optimal policy is then obtained by optimizing the switching times of this generalized strategy. In the case of a newly emerged disease, these results can be used in a data-driven receding horizon manner to improve actions as more data becomes available. The proposed approach is applied to publicly available data from the outbreak of SARS-CoV-2 in Germany. In particular, for minimizing the total number of infections or the number of isolated individuals, the simulations indicate that a sufficiently delayed and controlled release of the lock-down are optimal for overcoming the outbreak. The approach can support public health authorities to plan quarantine control policies.

The Disease Outbreak Channel of Exchange Rate Return Predictability: Evidence from COVID-19

ABSTRACT We provide novel evidence that disease outbreaks contain valuable information that can be used to enhance exchange rate return and volatility predictions. Our analysis exploits the novel coronavirus (COVID-19) outbreak as a good experimental setup to test our intuition. Data show that the COVID-19 outbreak has been rapid and deadly. Using the total number of infections per million, we demonstrate that COVID-19 has better predictive power over volatility than over returns for a one-day ahead forecast horizon. Conversely, COVID-19 tends to shape returns more than volatility over a five-day ahead forecast horizon. Our findings remain intact over the two forecast horizons using the total number of deaths per million as an alternative COVID-19 measure. This evidence supports a new channel of exchange rate return predictability, namely the disease outbreak channel.