Acute pancreatitis in 60 Iranian children: do pediatricians follow the new guidelines in diagnosis and management of acute pancreatitis? (preprint)

Background: the incidence of acute pancreatitis in children is increasingو but causes and diagnostic and therapeutic methods are different in different centers. The aim of this study was to investigate the common causes and routine diagnostic and therapeutic methods of acute pancreatitis in children in a pediatric gastrointestinal referral center and its compliance with existing guidelines. Methods: In this retrospective, cross-sectional study, a total of 60 children with a diagnosis of acute pancreatitis, were studied. Results: The most common causes of acute pancreatitis were systemic and metabolic diseases and medications. CT scan was performed for 36% of patients, but 31% of patients, for whom a CT scan was performed had no clear indication of CT scan. Only half of the patients received fluid 1.5 times their maintenance in the first 24 hours. Antibiotic therapy was performed for 48% of patients but medical indications for antibiotic treatment were found in only 34% of cases. During the COVID-19 pandemic, the relative incidence of acute pancreatitis was increased. Conclusions: In children with systemic and metabolic disease and using anticonvulsant drugs, it is important to consider the incidence of this disease. In clinical education, the risks of radiation due to unnecessary CT scans and inappropriate prescription of antibiotics need to be emphasized. More research should be done to study the association between COVID-19 and acute pancreatitis.

Optimal non-pharmaceutical intervention policy for Covid-19 epidemic via neuroevolution algorithm (preprint)

National responses to the Covid-19 pandemic varied markedly across countries, from business-as-usual to complete shutdowns. Policies aimed at disrupting the viral transmission cycle and preventing the healthcare system from being overwhelmed, simultaneously exact an economic toll. We developed a intervention policy model that comprised the relative human, economic and healthcare costs of non-pharmaceutical epidemic intervention and arrived at the optimal strategy using the neuroevolution algorithm. The proposed model finds the minimum required reduction in contact rates to maintain the burden on the healthcare system below the maximum capacity. We find that such a policy renders a sharp increase in the control strength at the early stages of the epidemic, followed by a steady increase in the subsequent ten weeks as the epidemic approaches its peak, and finally control strength is gradually decreased as the population moves towards herd immunity. We have also shown how such a model can provide an efficient adaptive intervention policy at different stages of the epidemic without having access to the entire history of its progression in the population. This work emphasizes the importance of imposing intervention measures early and provides insights into adaptive intervention policies to minimize the economic impacts of the epidemic without putting an extra burden on the healthcare system.

Ignoring spatial heterogeneity in drivers of SARS-CoV-2 transmission in the US will impede sustained elimination (preprint)

To dissect the transmission dynamics of SARS-CoV-2 in the United States, we integrate parallel streams of high-resolution data on contact, mobility, seasonality, vaccination and seroprevalence within a metapopulation network. We find the COVID-19 pandemic in the US is characterized by a geographically localized mosaic of transmission along an urban-rural gradient, with many outbreaks sustained by between-county transmission. We detect a dynamic tension between the spatial scale of public health interventions and population susceptibility as pre-pandemic contact is resumed. Further, we identify regions rendered particularly at risk from invasion by variants of concern due to spatial connectivity. These findings emphasize the public health importance of accounting for the hierarchy of spatial scales in transmission and the heterogeneous impacts of mobility on the landscape of contagion risk.

The Epidemic Volatility Index: an early warning tool for epidemics (preprint)

Background: . This paper presents, for the first time, the Epidemic Volatility Index (EVI), a conceptually simple, early warning tool for emerging epidemic waves. Methods: . EVI is based on the volatility of the newly reported cases per unit of time, ideally per day, and issues an early warning when the rate of the volatility change exceeds a threshold. Results: . Results from the COVID-19 epidemic in Italy and New York are presented here, while daily updated predictions for all world countries and each of the United States are available online. Interpretation . EVI’s application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting oncoming waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act fast and optimize containment of outbreaks.

Immunological heterogeneity informs estimation of the durability of COVID-19 vaccine protection (preprint)

Deciphering the properties of vaccines against coronavirus disease 2019 (COVID-19) is essential to predict the future course of the pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, current uncertainties about COVID-19 vaccine immunity raise the question of how much time will be needed to estimate these properties, in particular the durability of vaccine protection. Here we designed a simulation study, based on empirically validated epidemiological models of SARS-CoV-2 transmission, to predict the impact of a breadth of vaccines with different mean duration (range: 2–5 years) and heterogeneity (coefficient of variation range: 50–100%) of protection against infection. We then assessed how confidently the duration of protection could be estimated under a range of epidemiological scenarios in the year following the start of mass immunization. We found that lower population mean and higher inter-individual variability facilitated estimation of the duration of vaccine protection. Across the vaccines tested, high waning and high heterogeneity permitted complete identification of the duration of protection; in contrast, low waning and low heterogeneity allowed only estimation of the fraction of vaccinees with rapid loss of immunity. These findings suggest that key aspects of COVID-19 vaccine immunity can be estimated with limited epidemiological data. More generally, they highlight that immunological heterogeneity can sensitively determine the impact of COVID-19 vaccines and, it is likely, of other vaccines.

Feedbacks between global supply chain disruption and the spread of SARS-CoV-2 (preprint)

The pandemic of COVID-19 has become one of the greatest threats to human health, causing severe disruptions in the global supply chain, and compromising health care delivery worldwide. Although government authorities sought to contain the spread of SARS-CoV-2, the virus that causes COVID-19, by restricting travel and in-person activities, failure to deploy time-sensitive strategies in ramping-up of critical resource production exacerbated the outbreak. Here, we analyze the interactive effects of supply chain disruption and infectious disease dynamics using coupled production and disease networks built on global data. We find that time-sensitive containment strategies could be created to balance objectives in pandemic control and economic losses, leading to a spatiotemporal separation of infection peaks that alleviate the societal impact of the disease. A lean resource allocation strategy is discovered that effectively counteracts the positive feedback between transmission and production such that stockpiles of health care resources may be manufactured and distributed to limit future shortage and disease. The study highlights the importance of cross-sectoral coordination and region-wise collaboration to optimally contain a pandemic while accounting for production.

Five approaches to the suppression of SARS-CoV-2 without intensive social distancing (preprint)

Initial efforts to mitigate transmission of SARS-CoV-2 relied on intensive social distancing measures such as school and workplace closures, shelter-in-place orders, and prohibitions on the gathering of people. Other non-pharmaceutical interventions for suppressing transmission include active case finding, contact tracing, quarantine, immunity or health certification, and a wide range of personal protective measures. Here we investigate the potential effectiveness of these alternative approaches to suppression. We introduce a conceptual framework represented by two mathematical models that differ in strategy. We find both strategies may be effective, although both require extensive testing and work within a relatively narrow range of conditions. Generalized protective measures such as wearing face masks, improved hygiene, and local reductions in density are found to significantly increase the effectiveness of targeted interventions.

Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action (preprint)

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully, and to use that as a basis to explore the nonlinear behavior. We use a nonlinear SEIR (Susceptible, Exposed, Infectious & Removed) transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics.

COVID-19 herd immunity strategies: walking an elusive and dangerous tightrope (preprint)

The rapid growth in cases of COVID-19 has threatened to overwhelm healthcare systems in multiple countries. In response, severely affected countries have had to consider a range of public health strategies achieved by implementing non-pharmaceutical interventions. Broadly, these strategies have fallen into two categories: i) "mitigation", which aims to achieve herd immunity by allowing the SARS-CoV-2 virus to spread through the population while mitigating disease burden, and ii) "suppression", aiming to drastically reduce SARS-CoV-2 transmission rates and halt endogenous transmission in the target population. Using an age-structured transmission model, parameterised to simulate SARS-CoV-2 transmission in the UK, we assessed the prospects of success using both of these approaches. We simulated a range of different non-pharmaceutical intervention scenarios incorporating social distancing applied to differing age groups. We found that it is possible to suppress SARS-CoV-2 transmission if social distancing measures are sustained at a sufficient level for a period of months. Our modelling did not support achieving herd immunity as a practical objective, requiring an unlikely balancing of multiple poorly-defined forces. Specifically, we found that: i) social distancing must initially reduce the transmission rate to within a narrow range, ii) to compensate for susceptible depletion, the extent of social distancing must be vary over time in a precise but unfeasible way, and iii) social distancing must be maintained for a long duration (over 6 months).