An Innovative System to Understand the Development of Epidemics Using GIS Spatial Analysis and Based on AI and Big Data

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The advent of the COVID-19 pandemic (C19) has put a strain on the tightness of the epidemiological forecasting algorithms. These predictive models are traditionally based on SIR (Susceptible, Infected, Removed) and its updates. However, they did not provide reliable answers, especially in the first delicate phase, in which governments must take rapid decisions that are deemed to affect deeply the development and the outcome of the outbreak. This inadequacy derives not only from the model itself; it is also and undoubtedly generated by the lack of correct and timely data. Moreover, on the onset of a new pandemic, the disease is not known or it is only partially known. The first problem is the attitude of predicting it a priori, assuming the trend starting from a known mathematical curve. This approach is flawed, because it is impossible to provide a truthful forecast at the beginning of the epidemics (or of a new wave of infections), when, however, it is necessary to act promptly. Though as expected, as the epidemic progresses and the situation becomes homogeneous, mathematical models of pure interpolation and also SIR give more and more correct results. But during an epidemic, producing precise diffusion forecasts, including information on the structure of the wave front and its speed, is of paramount importance to organize an effective containment response.

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The global impact of COVID-19 has been profound, and the public health threat it represents is the most serious seen in a respiratory virus since the 1918 H1N1 influenza pandemic. Here we present the results of epidemiological modelling which has informed policymaking in the UK and other countries in recent weeks. In the absence of a COVID-19 vaccine, we assess the potential role of a number of public health measures-so-called non-pharmaceutical interventions (NPIs)-aimed at reducing contact rates in the population and thereby reducing transmission of the virus. In the results presented here, we apply a previously published microsimulation model to two countries: the UK (Great Britain specifically) and the US. We conclude that the effectiveness of any one intervention in isolation is likely to be limited, requiring multiple interventions to be combined to have a substantial impact on transmission. Two fundamental strategies are possible: (a) mitigation, which focuses on slowing but not necessarily stopping epidemic spread-reducing peak healthcare demand while protecting those most at risk of severe disease from infection, and (b) suppression, which aims to reverse epidemic growth, reducing case numbers to low levels and maintaining that situation indefinitely. Each policy has major challenges. We find that that optimal mitigation policies (combining home isolation of suspect cases, home quarantine of those living in the same household as suspect cases, and social distancing of the elderly and others at most risk of severe disease) might reduce peak healthcare demand by 2/3 and deaths by half. However, the resulting mitigated epidemic would still likely result in hundreds of thousands of deaths and health systems (most notably intensive care units) being overwhelmed many times over. For countries able to achieve it, this leaves suppression as the preferred policy option. We show that in the UK and US context, suppression will minimally require a combination of social distancing of the entire population, home isolation of cases and household quarantine of their family members. This may need to be supplemented by school and university closures, though it should be recognised that such closures may have negative impacts on health systems due to increased absenteeism. The major challenge of suppression is that this type of intensive intervention package-or something equivalently effective at reducing transmission-will need to be maintained until a vaccine becomes available (potentially 18 months or more)-given that we predict that transmission will quickly rebound if interventions are relaxed. We show that intermittent social distancing-triggered by trends in disease surveillance-may allow interventions to be relaxed temporarily in relative short time windows, but measures will need to be reintroduced if or when case numbers rebound. Last, while experience in China and now South Korea show that suppression is possible in the short term, it remains to be seen whether it is possible long-term, and whether the social and economic costs of the interventions adopted thus far can be reduced.
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Objectives: United States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States. Design: A nationwide, cross-sectional study using county-level data. Data sources: COVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) up to April 22, 2020 from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center. Main outcome measures: We fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses. Results: We found that an increase of only 1 μg/m3 in PM2.5 is associated with an 8% increase in the COVID-19 death rate (95% confidence interval [CI]: 2%, 15%). The results were statistically significant and robust to secondary and sensitivity analyses. Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite the inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.
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Speaking may be a primary mode of transmission of SARS-CoV-2. Considering that reports of asymptomatic transmission account for 50-80% of COVID-19 cases and that saliva has peak viral loads at time of patient presentation, droplet emission while speaking could be a significant factor driving transmission and warrants further study. We used a planar beam of laser light passing through a dust-free enclosure to detect saliva droplets emitted while speaking. We found that saying the words 'Stay Healthy' generates thousands of droplets that are otherwise invisible to the naked eye. A damp homemade cloth face mask dramatically reduced droplet excretion, with none of the spoken words causing a droplet rise above the background. Our preliminary findings have important implications for pandemic mitigation efforts.
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We statistically investigate the Coronavirus Disease 19 (hereinafter Covid-19) epidemics, which is particularly invasive in Italy. We show that the high apparent mortality (or Case Fatality Ratio, CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of infected cases. To give a more realistic estimate of the mortality of Covid-19, we use the most recent estimates of the IFR (Infection Fatality Ratio) of epidemic, based on the minimum observed CFR, and furthermore analyse data obtained from the ship Diamond Princess, a good representation of a ‘laboratory’ case-study from an isolated system in which all the people have been tested. From such analyses we derive more realistic estimates of the real extension of the infection, as well as more accurate indicators of how fast the infection propagates. We then point out from the various explanations proposed, the dominant factors causing such an abnormal seriousness of the disease in Italy. Finally, we use the deceased data, the only ones estimated to be reliable enough, to predict the total number of infected people and the interval of time when the infection in Italy could stop.
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We identified seasonal human coronaviruses, influenza viruses and rhinoviruses in exhaled breath and coughs of children and adults with acute respiratory illness. Surgical face masks significantly reduced detection of influenza virus RNA in respiratory droplets and coronavirus RNA in aerosols, with a trend toward reduced detection of coronavirus RNA in respiratory droplets. Our results indicate that surgical face masks could prevent transmission of human coronaviruses and influenza viruses from symptomatic individuals.
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To the Editor A novel human coronavirus, now named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, referred to as HCoV-19 here) that emerged in Wuhan, China in late 2019 is now causing a pandemic ¹ . Here, we analyze the aerosol and surface stability of HCoV-19 and compare it with SARS-CoV-1, the most closely related human coronavirus. ² We evaluated the stability of HCoV-19 and SARS-CoV-1 in aerosols and on different surfaces and estimated their decay rates using a Bayesian regression model (see Supplementary Appendix). All experimental measurements are reported as mean across 3 replicates.
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This thesis has two main goals: First, to construct an epidemic model for the Dengue disease and provide this as a user-friendly tool. Second, to investigate the parallelization methodologies for the generated model in terms of implementation effort and achieved speedup/acceleration. The resulting model is a multi-layer host SIR + vector SI CA (Cellular Automata) model with births/deaths, environmental, climate and population data parameters. Input data is integrated with normalization and feature scaling techniques. Population dynamics are simulated using Moore neighborhood based commuting micromovements. The model is highly scalable regarding the population sizes and the level of detail. The most influencing factor on the speed of simulation is the size of the grid. The ratio mosquito/human count per cell, was the most important factor for the disease spread. The infection dies out in most cases, if this ratio decreases below 0.05, as also noted by the literature [2]. The parallelization technique applied on the CA model is "Data Parallelism"[53, 54]. We investigated and optimized the performance of computationally intensive simulations, by using parallelization on CPU (Central Processing Unit) and GPGPU (General- Purpose computing on Graphics Processing Units) resources. Speed up gains using CPU and GPU parallelism with the STL library on a desktop computer ranged from 4x with CPU parallelism up to 13x-94x with GPU Parallelism. Performance was improved by replacing the STL library in C++ 11 by the Random Generator Sitmo PRNG [16, 17]. Using the Sitmo PRNG in multiple platforms, speedups are reported from 2x-4x with CPU parallelism and 2x-12x with GPU Parallelism. We started from execution times around 2-13 minutes and ended up with 2-9 seconds. CPU parallelization with Intel TBB is recommended if a quick way to increase performance is required. The TBB template library provides ready-to-use and threadsafe algorithms and requires minimal thread management. Existing object-oriented serial C++ source code can easily be transformed into parallel TBB code. GPGPU parallelization with Cuda is recommended in cases if "as fast as possible" batch executions are needed. The added benefit of the GPGPU approach for this model will increase in the future with additional model features and calculations. The downside is the requirement to change the algorithm logic, transform object oriented code to lower level languages and the need for specialized debugging and profiling tools.
Since the beginning of the COVID-19 epidemic in Italy, the Italian Government implemented several restrictive measures to contain the spread of the infection. Data shows that, among these measures, the lockdown implemented as of 9 March had a positive impact, in particular the central and southern regions of Italy, while other actions appeared to be less effective. When the true prevalence of a disease is unknown, it is possible estimate it, based on mortality data and the assumptive case-fatality rate of the disease. Given these assumptions, the estimated period-prevalence of COVID-19 in Italy varies from 0.35% in Sicity to 13.3% in Lombardy.
Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p<0·0001), and d-dimer greater than 1 μg/L (18·42, 2·64–128·55; p=0·0033) on admission. Median duration of viral shedding was 20·0 days (IQR 17·0–24·0) in survivors, but SARS-CoV-2 was detectable until death in non-survivors. The longest observed duration of viral shedding in survivors was 37 days. Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/L could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.
Background: A significant number of infectious diseases display seasonal patterns in their incidence, including human coronaviruses. Betacoronaviruses such as MERS-CoV and SARS-CoV are not thought to be seasonal. Methods: We examined climate data from cities with significant community spread of COVID-19 using ERA-5 reanalysis, and compared to areas that are either not affected, or do not have significant community spread. Findings: To date, Coronavirus Disease 2019 (COVID-19), caused by SARS-CoV-2, has established significant community spread in cities and regions along a narrow east west distribution roughly along the 30-50o N' corridor at consistently similar weather patterns consisting of average temperatures of 5-11oC, combined with low specific (3-6 g/kg) and absolute humidity (4-7 g/m3). There has been a lack of significant community establishment in expected locations that are based only on population proximity and extensive population interaction through travel. Interpretation: The distribution of significant community outbreaks along restricted latitude, temperature, and humidity are consistent with the behavior of a seasonal respiratory virus. Additionally, we have proposed a simplified model that shows a zone at increased risk for COVID-19 spread. Using weather modeling, it may be possible to predict the regions most likely to be at higher risk of significant community spread of COVID-19 in the upcoming weeks, allowing for concentration of public health efforts on surveillance and containment.
Currently, the emergence of a novel human coronavirus, temporary named 2019-nCoV, has become a global health concern causing severe respiratory tract infections in humans. Human-to-human transmissions have been described with incubation times between 2-10 days, facilitating its spread via droplets, contaminated hands or surfaces. We therefore reviewed the literature on all available information about the persistence of human and veterinary coronaviruses on inanimate surfaces as well as inactivation strategies with biocidal agents used for chemical disinfection, e.g. in healthcare facilities. The analysis of 22 studies reveals that human coronaviruses such as Severe Acute Respiratory Syndrome (SARS) coronavirus, Middle East Respiratory Syndrome (MERS) coronavirus or endemic human coronaviruses (HCoV) can persist on inanimate surfaces like metal, glass or plastic for up to 9 days, but can be efficiently inactivated by surface disinfection procedures with 62-71% ethanol, 0.5% hydrogen peroxide or 0.1% sodium hypochlorite within 1 minute. Other biocidal agents such as 0.05-0.2% benzalkonium chloride or 0.02% chlorhexidine digluconate are less effective. As no specific therapies are available for 2019-nCoV, early containment and prevention of further spread will be crucial to stop the ongoing outbreak and to control this novel infectious thread. FREE ACCESS ON JOURNAL HOMEPAGE
In this work are presented some methods for the monitoring and analysis of complex information related to the effects of the pollution on health. Different are the data that must be treated: they can be structured information but also unstructured and they regard both the health life of people but also the emission of pollutants and the effect of them on the individual health. Models about the emission and the dispersion of pollutants on the atmosphere and the effect of many substances on the individual health have been well analysed. What is missing is a comprehensive representation in which all these data and models are assessed in the light of new technologies. The starting point is the understanding of the intrinsic nature of information necessary and treated in order to know the evolution of complex events and the effect on the individual. Pollution emission, anthropic activities, lifestyles, urbanization and population are entities that are located on the territory. In the same way, meteorological condition during the time modifies the dispersion of the pollutants; the dispersion model involves human activities and portions of population. The description of the variation of these entities during the time is represented with graphical features on the maps representing the places in which people live and work producing visual impact and providing useful information to policy makers. More of these functions are realized by geographic information systems (GIS) but not only. In recent years we are witnessing the collection (voluntary or involuntary) of a plethora of information: data from sensors, personal devices that add useful information that must be treated. The capacity of integration of different source of information, the ability to treat complex information of different format shaped in huge data, the aptitude to integrate mathematical models in order to describe complex events, the possibility to geolocalize events and provide graphical outcome to explain complex physical events associated to health are the main challenges that this work addresses. This want to be a contribute not only for the clinicians in order to consider tools to manage the effect of the pollution to the people health but also a hint for the policy makers and researchers in order to invite to consider big problems affecting our cities from a comprehensive point of view.
COMPARE (COMPuter Aided Risk Evaluation) is a prototype of an integrated tool for developing hazard assessment and risk evaluation using various modelling techniques, scenario simulations and spatial analysis. It is a geographical information system (GIS) that integrates modules for data preparation and input, modelling, spatial analysis, and data display. The package can store the geographical information about the site under consideration, the related meteorological data, a detailed plan of the industrial installations, and a data base containing the descriptions of the source points of accidental releases. The user can run a consequence model chosen from the set integrated in the package, with parameters automatically selected by the system. The output is a contour plot of the consequences of the analyzed event on the map of the site. COMPARE has been used to verify compliance of industrial installations with Italian law, as far as relevant risks are concerned.
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