Uncertainty quantification in mechanistic epidemic models via cross-entropy approximate Bayesian computation

This paper proposes a data-driven approximate Bayesian computation framework for parameter estimation and uncertainty quantification of epidemic models, which incorporates two novelties: (i) the identification of the initial conditions by using plausible dynamic states that are compatible with observational data; (ii) learning of an informative prior distribution for the model parameters via the cross-entropy method. The new methodology's effectiveness is illustrated with the aid of actual data from the COVID-19 epidemic in Rio de Janeiro city in Brazil, employing an ordinary differential equation-based model with a generalized SEIR mechanistic structure that includes time-dependent transmission rate, asymptomatics, and hospitalizations. A minimization problem with two cost terms (number of hospitalizations and deaths) is formulated, and twelve parameters are identified. The calibrated model provides a consistent description of the available data, able to extrapolate forecasts over a few weeks, making the proposed methodology very appealing for real-time epidemic modeling

Brain functional connectivity alterations associated with neuropsychological performance 6-9 months following SARS-CoV-2 infection.

Neuropsychological deficits and brain damage following SARS-CoV-2 infection are not well understood. Then, 116 patients, with either severe, moderate, or mild disease in the acute phase underwent neuropsychological and olfactory tests, as well as completed psychiatric and respiratory questionnaires at 223 ± 42 days postinfection. Additionally, a subgroup of 50 patients underwent functional magnetic resonance imaging. Patients in the severe group displayed poorer verbal episodic memory performances, and moderate patients had reduced mental flexibility. Neuroimaging revealed patterns of hypofunctional and hyperfunctional connectivities in severe patients, while only hyperconnectivity patterns were observed for moderate. The default mode, somatosensory, dorsal attention, subcortical, and cerebellar networks were implicated. Partial least squares correlations analysis confirmed specific association between memory, executive functions performances and brain functional connectivity. The severity of the infection in the acute phase is a predictor of neuropsychological performance 6-9 months following SARS-CoV-2 infection. SARS-CoV-2 infection causes long-term memory and executive dysfunctions, related to large-scale functional brain connectivity alterations

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of “what-if” scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY—one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches—in hospitals or drive-through facilities—and vaccination strategies that could prioritize vulnerable groups. Decision-making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features

High-Resolution Agent-Based Modeling of COVID-19 Spreading in a Small Town

Amid the ongoing COVID-19 pandemic, public health authorities and the general population are striving to achieve a balance between safety and normalcy. Ever changing conditions call for the development of theory and simulation tools to finely describe multiple strata of society while supporting the evaluation of "what-if" scenarios. Particularly important is to assess the effectiveness of potential testing approaches and vaccination strategies. Here, an agent-based modeling platform is proposed to simulate the spreading of COVID-19 in small towns and cities, with a single-individual resolution. The platform is validated on real data from New Rochelle, NY -- one of the first outbreaks registered in the United States. Supported by expert knowledge and informed by reported data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches -- in hospitals or drive-through facilities -- and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.Comment: 44 pages (including 16 of Supplementary Information). Published online in Advanced Theory and Simulation

Mitigating aerosol infection risk in school buildings: the role of natural ventilation, volume, occupancy and CO2 monitoring

Issues linked to aerosol physics within school buildings and related infection risk still lack a proper recognition in school safety regulations. Limited spaces and limited available window-surfaces require to precisely investigate the seasonal airing factors and the occupancy/volume ratios in each classroom in order to assess the specific risk levels from viral loads of potentially infective sources. Moreover, most schools are still not provided with mechanical HVAC systems nor with air quality sensors. Fundamental questions are therefore: how the specific classroom volume and the specific airing cycle affects the long-range contagion risk in a given classroom? is linear social distancing the right way to assess a volumetric risk problem? We present here the results of an extended quantitative analysis based on the GN-Riley infection risk model applied to a real classroom scenario. The study discusses seasonality of the airing flow and the effectiveness of single and combined mitigation interventions, such as limiting student groups, equipping teachers with microphones, increasing classroom volumes, and equipping classrooms with CO2 sensors to safely drive airing intervals. Moreover, we show experimental CO2 concentrations as well as occupancy and airing factors monitored in real time in a real classroom scenario. In agreement with recent literature, the results emphasize the need for a dynamic evaluation of the complex risk function over the whole exposure time (and not just the monitoring of the istanteneous CO2 concentration) in order to correctly control the infection risk from aerosolization

High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation

This paper presents an approach for the modeling and the simulation of the spreading of COVID-19 based on agent-based modeling and simulation (ABMS). Our goal is not only to support large-scale simulations but also to increase the simulation resolution. Moreover, we do not assume an underlying network of contacts, and the person-to-person contacts responsible for the spreading are modeled as a function of the geographical distance among the individuals. In particular, we defined a commuting mechanism combining radiation-based and gravity-based models and we exploited the commuting properties at different resolution levels (municipalities and provinces). Finally, we exploited the high-performance computing (HPC) facilities to simulate millions of concurrent agents, each mapping the individual’s behavior. To do such simulations, we developed a spreading simulator and validated it through the simulation of the spreading in two of the most populated Italian regions: Lombardy and Emilia-Romagna. Our main achievement consists of the effective modeling of 10 million of concurrent agents, each one mapping an individual behavior with a high-resolution in terms of social contacts, mobility and contribution to the virus spreading. Moreover, we analyzed the forecasting ability of our framework to predict the number of infections being initialized with only a few days of real data. We validated our model with the statistical data coming from the serological analysis conducted in Lombardy, and our model makes a smaller error than other state of the art models with a final root mean squared error equal to 56,009 simulating the entire first pandemic wave in spring 2020. On the other hand, for the Emilia-Romagna region, we simulated the second pandemic wave during autumn 2020, and we reached a final RMSE equal to 10,730.11

Bio-surveillance Capability Requirements for the Global Health Security: Study on Epidemiological Differences of COVID-19 Cases

Background: Just eleven months after the first reported COVID-19 infection, the global tally has surpassed 60 million cases with a global death toll standing at 1.4 million. Even though with the launch of the Global Health Security Agenda in 2014, only 67 countries came under the umbrella of this agenda and trying to exchange as well as integrate various strengths to fight against massive threats of multiple infectious diseases. The current Covid-19 pandemic basically exposed the paucity of capacities and capabilities of nations’ Bio-surveillance System, even the so-called developed ones. Method: Cross-sectional study was carried out within the time period of the 16th September - 30th November 2020, taking into account the secondary data of COVID-19 patients up to 22nd April 2020, in South Korea, Australia, & England as sample population. After the extensive analysis of the data-driven from the authorized websites of the three countries - the Incidence Rate (%) and Case Fatality Rate (%) according to age and sex groups were compared along with Crude Incidence Rate, Crude Mortality Rate, Age & Sex-Specific Incidence Rate, Age & Sex-Specific Mortality Rate, Age & Sex Adjusted Incidence rate, Age & Sex Adjusted Mortality rate, by plotting into charts and graphs. Results: In the case of all three countries, Incidence Rates are increasing with the increase in age of the population. Except for the female of South Korea, the incidence of COVID-19 in both the other two countries were high in case of the male population. the mortality rate of male patients was higher than female patients in all age groups in all three countries. In the case of England, the Incidence Rate (%) and Case Fatality Rate (%) according to age and sex groups along with Crude Incidence Rate, Crude Mortality Rate, Age & Sex-Specific Incidence Rate, Age & Sex-Specific Mortality Rate, Age & Sex Adjusted Incidence rate, Age & Sex Adjusted Mortality Rate all are 30 to more than 100 times higher than Australia and South Korea. Australia shows the lowest in COVID-19 infection and death rates among the countries in all aspects. Conclusion: This study shows the gaps of currently available bio-surveillance methods leading to an uncontrolled and unprecedented surge of the ongoing COVID-19 contagion and fatality world wide, driving mankind into an uncertain future. Ameliorating the currently available bio-hazard and disease surveillance system by filling those gaps up along with the help of continuous need-based research and innovations, imply tremendous importance to overcome the current situation and to predict upcoming “Disease-X” threats.open석

Application-based COVID-19 micro-mobility solution for safe and smart navigation in pandemics

Short distance travel and commute being inevitable, safe route planning in pandemics for micro-mobility, i.e., cycling and walking, is extremely important for the safety of oneself and others. Hence, we propose an application-based solution using COVID-19 occurrence data and a multi-criteria route planning technique for cyclists and pedestrians. This study aims at objectively determining the routes based on various criteria on COVID-19 safety of a given route while keeping the user away from potential COVID-19 transmission spots. The vulnerable spots include places such as a hospital or medical zones, contained residential areas, and roads with a high connectivity and influx of people. The proposed algorithm returns a multi-criteria route modeled on COVID-19-modified parameters of micro-mobility and betweenness centrality considering COVID-19 avoidance as well as the shortest available safe route for user ease and shortened time of outside environment exposure. We verified our routing algorithm in a part of Delhi, India, by visualizing containment zones and medical establishments. The results with COVID-19 data analysis and route planning suggest a safer route in the context of the coronavirus outbreak as compared to normal navigation and on average route extension is within 8%–12%. Moreover, for further advancement and post-COVID-19 era, we discuss the need for adding open data policy and the spatial system architecture for data usage, as a part of a pandemic strategy. The study contributes new micro-mobility parameters adapted for COVID-19 and policy guidelines based on aggregated contact tracing data analysis maintaining privacy, security, and anonymity