TRANSBOUNDARY AND EMERGING DISEASES 04/02/21 A multi‐regional, hierarchical‐tier mathematical model of the spread and control of COVID‐19 epidemics from epicenter to adjacent regions. INRAE 21/12/20 Epidémie de COVID-19 : mise au point d’un modèle de prédiction de la propagation. *Ecole des Hautes Etudes en Sciences Sociales Les données disponibles sur le nombre de décès dus à l’épidémie de COVID-19 montrent généralement des disparités régionales.
Les premiers clusters observés en février 2020 se situent dans le Grand Est et en Île-de-France, et la propagation spatiale de la maladie semble avoir suivi ces premières observations. Le 17 mars 2020, la France est entrée dans un confinement strict qui a permis de diviser par 5 à 7 la diffusion du virus dans le pays, en restreignant les déplacements et les contacts entre personnes. En parallèle, la généralisation du port du masque et d’autres mesures sanitaires réduisent la probabilité d’infection lors de contacts entre personnes.
Par la suite, des mesures sanitaires ont été appliquées au niveau local, par département, en s’appuyant sur les données de terrain comme le nombre de personnes testées positives. 1 Nombre moyen de personnes qu’une personne infectée contamine. MEDRXIV 22/09/20 COVID-19: Risks of Re-emergence, Re-infection, and Control Measures. BIOREMEDIATION SCIENCE AND TECHNOLOGY RESEARCH 31/07/20 Predictive Mathematical Modelling of the Total Number of COVID-19 Cases for The United States. Hafeez Muhammad Yakasai Department of Biochemistry, Faculty of Basic Medical Sciences, College of Health Science, Bayero University Kano PMB 3011, Nigeria.
Mohd Yunus Abd Shukor Department of Biochemistry, Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, D.E, Malaysia. Keywords: Total infection, COVID-19, Pandemic, MMF, mathematical model Abstract. FRONT. VET. SCI. 25/08/20 Prediction Models in Veterinary and Human Epidemiology: Our Experience With Modeling Sars-CoV-2 Spread. Introduction Infectious diseases are a constant threat for public health and consequently also the economy.
Although hygiene measures have been well-established and efficient prevention and control measures such as vaccines have been developed for many diseases, only one human disease (smallpox) and one animal disease (Rinderpest) have been eradicated (1). On the other hand, new diseases are emerging and re-emerging in several parts of the world in both humans (e.g., COVID-19) and animals [e.g., African swine fever (ASF) ]. It is therefore important to have consistent and effective systems for rapid and successful control of infectious diseases of both humans and animals. Models of infectious diseases have been used for many years to understand the dynamics of these diseases and to support decision making, and are used in both animal and human populations (2, 3). Int J Infect Dis. 2020 Jun 20; Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020)
Data for the 24 countries with the highest confirmed total cases (more than 50,000), deaths, total recovered, active cases, serious/critical, and test rates per 0.1 M pop (as of June 13, 2020) were compiled ( and analyzed (Table 1).
Results are Belgium – 83.32, UK – 61.39, Spain – 58.04, Italy – 56.73, Sweden – 48.28, France – 45.04, USA – 35.52, Canada – 21.49, Brazil – 20.10, Peru – 19.70, Chile – 16.20, Mexico– 12.80, Iran – 10.40, and Germany – 10.58. The lowest deaths/0.1 M pop among these top 24 countries were between 0.3 and 5.7 in China – 0.3, Bangladesh – 0.7, India – 0.7, Pakistan – 1.20, South Africa – 2.40, Qatar – 2.50, Saudi Arabia – 2.70, Russia – 4.70, and Turkey – 5.70. Iran’s portions of the world pandemic were 2.35% of all active cases, 2.02% of all deaths, 3.64% of all recovered, 0.87% of active cases, 5.09% of serious/critical cases, and 220.3 total cases per 0.1 M population. Enria et al., 1998 Enria D.A. MEDRXIV 28/07/20 A Bayesian Framework for Estimating the Risk Ratio of Hospitalization for People with Comorbidity Infected by the SARS-CoV-2 Virus.
STATNEWS 21/07/20 Public health group calls for standardized data collection to more clearly track Covid-19. In a new review of the Covid-19 response across the country, a group of public health experts conclude that critical data the public needs to assess their risks and tailor their behaviors is often unavailable.
The assessment, released Tuesday by the nongovernmental organization Resolve to Save Lives, calls on states and communities to start recording and sharing standardized data on 15 key metrics, so that people — and health departments — can get a clearer picture of how the response to the pandemic is working in their area. Tom Frieden, president and CEO of Resolve, which is an initiative of the global health organization Vital Strategies, said there is currently both a glut of data and a scarcity of information — a situation that needs to change if the country has any hope of gaining ground against the SARS-CoV-2 virus. advertisement “I don’t know how you can judge where you’re at if you don’t have this kind of information,” he said.
“What gets measured can get managed. MEDRXIV 19/07/20 Modeling the progression of SARS-CoV-2 infection in patients with COVID-19 risk factors through predictive analysis. COLUMBIA_EDU 17/06/20 Special Webinar (Archived Recording) – Modeling of COVID-19 and Other Infectious Diseases. INFECTIOUS DISEASES OF POVERTY 23/06/20 COVID-19 seeding time and doubling time model: an early epidemic risk assessment tool.
Seeding number A total of 30 countries met the criteria for inclusion in the determination of median SN.
These 30 countries’ epidemiologic curves, epidemic “take-off” points, and individual SNs are shown in Fig. 2. The overall median SN for the 30 countries was 12 days (range: 3–28; interquartile range [IQR]: 10–17). Hence, the SN used for the ST/DT Model was set to 12 cases. Raw and summarized data used to calculate overall median SN can be found in in the Additional file 1: Table S2 and Table S3 . Epidemiologic curves of 30 countries used to determine seeding number (SN). Mean seeding time and mean doubling time Among these 30 countries, a total of 20 were used to determine mean ST and mean DT. FRANCE STRATEGIE VIA YOUTUBE 06/06/20 Covid-19 : quelle utilité des modèles pour la gestion de crise et pour la prévention des risques ?
MEDRXIV 22/05/20 Mathematical Modeling and Analysis of COVID-19 pandemic in Nigeria. MEDRXIV 03/06/20 Predicting the COVID-19 epidemic in Algeria using the SIR model. MEDRXIV 27/05/20 COVID-19: The unreasonable effectiveness of simple models. LE MONDE 26/05/20 Les modèles déboussolés pour prédire l’évolution de l’épidémie due au coronavirus.
Jour après jour, l’épidémie de Covid-19 perd du terrain mais est-elle pour autant derrière nous ?
Voilà toute la question, alors que le gouvernement s’apprête à annoncer, dans les prochains jours, de nouveaux arbitrages pour le « chapitre II » du déconfinement, qui doit commencer le 2 juin. Pour y répondre, les épidémiologistes disposent de différents indicateurs mais, deux semaines seulement après la levée du confinement, leur interprétation est encore incertaine. Premier baromètre, le nombre quotidien d’admissions en réanimation. Il s’élevait à plus de 700 au pic de l’épidémie début avril ; il n’était plus que de 45 le 25 mai. Ce point bas était anticipé par les modèles, compte tenu du délai entre les nouvelles infections et l’arrivée en réanimation des cas les plus graves. « C’est le reflet des contaminations qui ont eu lieu à la toute fin du confinement ou au début du déconfinement.
MEDRXIV 19/05/20 Detecting the Emergent or Re-Emergent COVID-19 Pandemic in a Country: Modelling Study of Combined Primary Care and Hospital Surveillance. BMC PUBLIC HEALTH 24/04/20 Can mathematical modelling solve the current Covid-19 crisis? Since COVID-19 transmission started in late January, mathematical modelling has been at the forefront of shaping the decisions around different non-pharmaceutical interventions to confine its’ spread in the UK.
One model in particular, developed by Neil Ferguson’s group at Imperial College London [1] has been widely quoted as the driving force behind the social-distancing measures implemented in the UK and worldwide in order to halt COVID-19 spread. As a mathematical modeller with vast experience in developing, parametrising, calibrating and using models to answer different policy questions, I have been excited with the power that this mathematical model has had.
But at the same time, knowing that mathematical modeling is designed to simplify reality and answer specific questions using relevant subsets of data, I had wondered how robust this mathematical model is, especially when the dataset they have used is only days, possibly a couple of months, long. MEDRXIV 29/04/20 Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil. MEDRXIV 27/04/20 Modeling geographical spread of COVID-19 in India using network-based approach. MEDRXIV 23/04/20 Epidemiological impact of SARS-CoV-2 vaccination: mathematical modeling analyses. MEDRXIV 20/04/20 Trends and prediction in daily incidence and deaths of COVID-19 in the United States: a search-interest based model. MEDRXIV 19/04/20 A Contribution to the Mathematical Modeling of the Corona/COVID-19 Pandemic.
MEDRXIV 06/04/20 Data model to predict prevalence of COVID-19 in Pakistan. LIVERPOOL_AC_UK 08/04/20 Researchers help develop heat map to monitor movement during COVID-19 pandemic. Researchers from the University’s Institute of Population Health Sciences and Department of Electrical Engineering and Electronics, working with colleagues at The University of Manchester and Evergreen Life Ltd, have developed a live map of COVID-19 symptoms from an app that connects patients to their GP records and asks them questions about their health.
The Evergreen Life app (www.e-life.co.uk) is used by patients to book GP appointments, order repeat prescriptions, access their NHS GP records and record personal wellbeing information. In response to COVID-19 the company started asking its app users about symptoms that might give early warning of how the pandemic is affecting communities. They pushed out questions via the app and asked our research teams to help them analyse the data and design further questions. Users of the app from across the UK are being asked to report if they are self-isolating, have a fever or a dry cough. The Heat Map is available here. MEDRXIV 31/03/20 Modeling the Corona Virus Outbreak in IRAN.
MEDRXIV 04/04/20 Modeling the COVID-19 outbreaks and the effectiveness of the containment measures adopted across countries. INTERNATIONAL JOURNAL OF STATISTICS AND MEDICAL INFORMATICS - 2020 - Overview of different models for predicting COVID-19 Cases. April 3, 2020 Journal article Open Access Editor IJSMI During the month of Dec 2019 Chinese people from Wuhan the capital of Hubei Province were affected by the Virus called ‘Covid-19’ (initially it was called as Novel Coronavirus (2019-nCoV)) which was originated from the Sea food market as per the Chinese Government reports.
During the first week of January 2020 Chinese Authorities identified this new virus [1]. It slowly started spreading to other parts of China. During the month January it started spreading to Thailand, South Korea and Japan through travellers from the Wuhan City [1]. Full paper can be accessed at. THECONVERSATION 01/04/20 Pour comprendre la pandémie, les courbes valent mieux que les avalanches de chiffres. Une avalanche de chiffres concernant l’épidémie de coronavirus nous submerge depuis la fin janvier, quand la ville de Wuhan a été placée en quarantaine. Chaque jour les médias ont égrené le nombre de morts et de nouveaux cas, d’abord à Wuhan, puis, à mesure que l’épidémie gagnait, dans la province du Hubei, la Chine tout entière, les pays voisins, l’Europe et le monde entier, pays par pays.
Cette épidémie de chiffres inquiétants nous a paradoxalement davantage caché la réalité qu’elle ne l’a révélée. Les courbes révèlent ce que les chiffres cachent L’un de nous, il doit l’avouer non sans embarras, a longtemps pensé que l’information en continu était davantage à blâmer que le virus lui-même et que l’on nous refaisait le coup de la grippe aviaire. Nous témoignons ici de la difficulté, pour des non-spécialistes en épidémiologie comme nous (fussent-ils scientifiques), à saisir la portée de la pandémie, face à son traitement médiatique. BREAKINGNEWS 02/04/20 les problèmes avec les modèles mathématiques derrière les stratégies de contrôle de covid-19 dans de nombreux pays. “Essentiellement, tous les modèles sont erronés, mais certains sont utiles”, a déclaré le statisticien britannique George Box en 1976. 44 ans plus tard, ses paroles ont de nouveau un sens au milieu de la crise de santé profonde qui frappe tout le monde face à la propagation rapide de Covid-19.
En termes simples, les modèles mathématiques sont des projections statistiques qui, sur la base d’une certaine quantité de données, nvous aider à faire des estimations concernant divers phénomènes ou processus. En science, et en particulier dans l’étude des maladies, ils sont souvent utilisés fréquemment car c’est un moyen efficace pour les chercheurs de comprendre à quoi ils ont affaire. Cependant, les modèles mathématiques ne sont pas toujours parfaits. Et dans le cas du nouveau coronavirus, il semble qu’au moins jusqu’à présent plusieurs d’entre eux ont donné de mauvais résultats. MEDRXIV 23/03/20 Predicting the evolution Of SARS-Covid-2 in Portugal using an adapted SIR Model previously used in South Korea for the MERS outbreak.
MEDRXIV 27/03/20 Modeling for Corona Virus Outbreak in IRAN. FRANCE INTER 24/03/20 L EDITO CARRE - Modélisation du coronavirus. Ce matin dans l'édito carré, comment les scientifiques étudient l’évolution du Coronavirus. GITHUB_IO 23/03/20 Real-time modeling and projections of the COVID-19 epidemic in Switzerland. IRD 16/03/20 Un modèle mathématique de l’épidémie de coronavirus en France.
Vet Q. 2020 Feb 8:1-12. Emerging novel Coronavirus (2019-nCoV) - Current scenario, evolutionary perspective based on genome analysis and recent developments. THE LANCET 11/03/20 Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Summary Background An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to 95 333 confirmed cases as of March 5, 2020.
Understanding the early transmission dynamics of the infection and evaluating the effectiveness of control measures is crucial for assessing the potential for sustained transmission to occur in new areas. Combining a mathematical model of severe SARS-CoV-2 transmission with four datasets from within and outside Wuhan, we estimated how transmission in Wuhan varied between December, 2019, and February, 2020. We used these estimates to assess the potential for sustained human-to-human transmission to occur in locations outside Wuhan if cases were introduced.
Methods We combined a stochastic transmission model with data on cases of coronavirus disease 2019 (COVID-19) in Wuhan and international cases that originated in Wuhan to estimate how transmission had varied over time during January, 2020, and February, 2020. Findings Interpretation. SOFT COMPUTING 13/01/20 A Bayesian network model for the reliability control of fresh food e-commerce logistics systems.
SCIENCE DAILY 31/01/20 Modeling study estimates spread of 2019 novel coronavirus. New modelling research, published in The Lancet, estimates that up to 75,800 individuals in the Chinese city of Wuhan may have been infected with 2019 novel coronavirus (2019-nCoV) as of January 25, 2020. Senior author Professor Gabriel Leung from the University of Hong Kong highlights: "Not everyone who is infected with 2019-nCoV would require or seek medical attention.
During the urgent demands of a rapidly expanding epidemic of a completely new virus, especially when system capacity is getting overwhelmed, some of those infected may be undercounted in the official register. " He explains: "The apparent discrepancy between our modelled estimates of 2019-nCoV infections and the actual number of confirmed cases in Wuhan could also be due to several other factors. The new estimates also suggest that multiple major Chinese cities might have already imported dozens of cases of 2019-nCoV infection from Wuhan, in numbers sufficient to initiate local epidemics. PLOS 14/02/20 A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. Citation: Sardar T, Ghosh I, Rodó X, Chattopadhyay J (2020) A realistic two-strain model for MERS-CoV infection uncovers the high risk for epidemic propagation. PLoS Negl Trop Dis 14(2): e0008065. Editor: Benjamin Althouse, Institute for Disease Modeling, UNITED STATES Received: December 14, 2018; Accepted: January 15, 2020; Published: February 14, 2020.
MEDRXIV 19/02/20 The estimate of infected individuals of the 2019-Novel Coronavirus in South Korea by incoming international students from the countries of risk of 2019-Novel Coronavirus: a simulation study. MEDRXIV 14/02/20 A spatial model of CoVID-19 transmission in England and Wales: early spread and peak timing. MEDRXIV 05/02/20 Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study. MEDRXIV 02/02/20 Early dynamics of transmission and control of 2019-nCoV: a mathematical modelling study. LE MONDE 17/03/20 Covid-19 : les scénarios décisifs de modélisateurs britanniques. Face à un virus tel que le SARS-CoV-2, contre lequel n’existent encore ni vaccin ni traitement à l’efficacité cliniquement éprouvée, quel serait l’impact de mesures non pharmaceutiques pour réduire la mortalité et la pression sur le système de santé ?
C’est la question à laquelle s’est attachée l’équipe de Neil Ferguson (Imperial College, Londres), spécialisée dans les modélisations des épidémies, et qui a fait porter ses analyses sur les cas du Royaume-Uni et des Etats-Unis. Déjà en surchauffe La réponse est glaçante : quelles que soient les stratégies mises en œuvre, le nouveau coronavirus aura des répercussions « profondes » et de longue durée sur ces pays et les nations comparables, et les mesures ne garantiront pas contre un éventuel rebond de l’épidémie. Lire aussi Coronavirus : les simulations alarmantes des épidémiologistes pour la France Une protection immunitaire collective ? LE MONDE 15/03/20 Coronavirus : les simulations alarmantes des épidémiologistes pour la France. CONTAGIONLIVE 16/03/20 COVID-19 Model Shows Travel Restrictions Work, But Testing and Isolation Key. New research based on mathematical modeling of SARS-CoV-2 transmission rates finds transmission declined by approximately half when China’s Wuhan province introduced travel restrictions.
The study also highlights the importance of testing and isolation as a defense against the spread of the virus. The team found that the median daily reproduction number of the virus dropped from 2.35 down to 1.05 within the week following the introduction of strict travel restrictions in Wuhan on January 23rd. They also found that when the virus is introduced to new locations, the first case or 2 don’t necessarily lead to outbreaks. CENTRE FOR MATHEMATICAL MODELLING OF INFECTIOUS DISEASES 17/03/20 Temporal variation in transmission during the COVID-19 outbreak in Italy.
BIORXIV 19/01/20 A mathematical model for simulating the transmission of Wuhan novel Coronavirus. BIOENGINEER 07/02/20 Evolution of Wuhan coronavirus (2019-nCoV) and modeling of spike protein for human transmission. The cluster of pneumonia cases in Wuhan City, Hubei Province of China was first reported on December 30, 2019 by the Wuhan Municipal Health Commission. The Centers for Disease Control and Prevention (CDC) later determined and announced a novel coronavirus (CoV), denoted as Wuhan CoV (2019-nCoV), had caused the outbreak of the pneumonia.
The current public health emergency partially resembles the emergence of the SARS outbreak in southern China in 2002, which led to more than 8,000 human infections and 774 deaths. As of January 15, 2020, there were more than 40 laboratory-confirmed cases of the noval Wuhan CoV infection with one death and multiple exported cases in Japan, and Thailand. Under the current public health emergency, it is imperative to understand the origin and native host(s) of the Wuhan CoV, and to evaluate the public health risk of this novel coronavirus for transmission cross species or between humans. See the article: Xu, X.T., Chen, P., Wang, J.F., et al. (2020).