Modelling COVID 19 in the Basque Country from introduction to control measure response

Abstract In March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque health managers and Government during the COVID-19 responses. BMTF is a modelling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. Here we describe the efforts and challenges to develop a flexible modeling framework able to describe the dynamics observed for the tested positive cases, including the modelling development steps. The results obtained by a new stochastic SHARUCD model framework are presented. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, providing important projections on the national health system’s necessities during the increased population demand on hospital admissions. Short and longer-term predictions were tested with good results adjusted to the available epidemiological data. We have shown that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate $$ \lambda $$ λ was calculated from the model and from the data and the implications for the reproduction ratio r are shown. The analysis of the growth rates from the data led to improved model versions describing after the exponential phase also the new information obtained during the phase of response to the control measures. This framework is now being used to monitor disease transmission while the country lockdown was gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining.

Download Full-text

Modeling COVID 19 in the Basque Country: from introduction to control measure response

10.1101/2020.05.10.20086504 ◽

2020 ◽

Cited By ~ 1

Author(s):

Maíra Aguiar ◽

Eduardo Millán Ortuondo ◽

Joseba Bidaurrazaga Van-Dierdonck ◽

Javier Mar ◽

Nico Stollenwerk

Keyword(s):

Disease Transmission ◽

Task Force ◽

Control Measure ◽

Disease Incidence ◽

Basque Country ◽

National Health System ◽

Epidemiological Data ◽

Model Framework ◽

Reproduction Ratio ◽

Incidence Data

AbstractIn March 2020, a multidisciplinary task force (so-called Basque Modelling Task Force, BMTF) was created to assist the Basque Health managers and the Basque Government during the COVID-19 responses. BMTF is a modeling team, working on different approaches, including stochastic processes, statistical methods and artificial intelligence. In this paper we describe and present the results obtained by a new stochastic SHARUCD model framework which was able to describe the disease incidence data provided by the Basque Health Services. Our models differentiate mild and asymptomatic from severe infections prone to be hospitalized and were able to predict the course of the epidemic, from introduction to control measure response, providing important projections on the national health system necessities during the increased population demand on hospital ad-missions. Short and longer-term predictions were tested with good results adjusted to the current epidemiological data, showing that the partial lockdown measures were effective and enough to slow down disease transmission in the Basque Country. The growth rate λ Is calculated from the model and from the data and the implications for the reproduction ratio r are shown. At the moment, the reproduction ratio r is estimated to be below the threshold behavior of r = 1, but still close to 1, meaning that although the number of new cases are decelerating, a careful monitoring of the development of the outbreak is required. This framework is now being used to monitor disease transmission while the country lock-down is gradually lifted, with insights to specific programs for a general policy of “social distancing” and home quarantining. These are the first publicly available modeling results for the Basque Country and the efforts will be continued taking into consideration the updated data and new information that are generated over time.

Download Full-text

Reproduction ratio and growth rates: measures for an unfolding pandemic

10.1101/2020.05.18.20105528 ◽

2020 ◽

Author(s):

Maíra Aguiar ◽

Joseba Bidaurrazaga Van-Dierdonck ◽

Nico Stollenwerk

Keyword(s):

Growth Rates ◽

Disease Transmission ◽

Task Force ◽

Basque Country ◽

Recovery Period ◽

Data Sets ◽

Reproduction Ratio ◽

Task Forces ◽

Reproduction Numbers ◽

Health Managers

AbstractThe initial exponential growth rate of an epidemic is an important measure that follows directly from data at hand, commonly used to infer the basic reproduction number. As the growth rates λ(t) of tested positive COVID-19 cases have crossed the threshold in many countries, with negative numbers as surrogate for disease transmission deceleration, lockdowns lifting are linked to the behavior of the momentary reproduction numbers r(t), often called R0. Important to note that this concept alone can be easily misinterpreted as it is bound to many internal assumptions of the underlying model and significantly affected by the assumed recovery period. Here we present our experience, as part of the Basque Country Modeling Task Force (BMTF), in monitoring the development of the COVID-19 epidemic, by considering not only the behaviour of r(t) estimated for the new tested positive cases - significantly affected by the increased testing capacities, but also the momentary growth rates for hospitalizations, ICU admissions, deceased and recovered cases, in assisting the Basque Health Managers and the Basque Government during the lockdown lifting measures. Two different data sets, collected and then refined during the COVID-19 responses, are used as an exercise to estimate the momentary growth rates and reproducetion numbers over time in the Basque Country, and the implications of using those concepts to make decisions about easing lockdown and relaxing social distancing measures are discussed. These results are potentially helpful for task forces around the globe which are now struggling to provide real scientific advice for health managers and governments while the lockdown measures are relaxed.

Download Full-text

Immunological characteristics govern the changing severity of COVID-19 during the transition to endemicity

10.1101/2020.09.03.20187856 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jennie S Lavine ◽

Ottar N Bjornstad ◽

Rustom Antia

Keyword(s):

Common Cold ◽

Severe Disease ◽

Herd Immunity ◽

Epidemiological Data ◽

Control Measures ◽

Vulnerable Groups ◽

Model Framework ◽

Benign Nature ◽

The Common ◽

Human Coronaviruses

As prospects for eradicating CoV-2 dwindle, we are faced with the question of how the severity of CoV-2 disease may change in the years ahead. Will CoV-2 continue to be a pathogenic scourge that, like smallpox or measles, can be tamed only by ongoing vaccination, or will it join the ranks of mild endemic human coronaviruses (HCoVs)? Our analysis of immunological and epidemiological data on HCoVs shows that infection-blocking immunity wanes rapidly, but disease-reducing immunity is long-lived. We estimate the relevant parameters and incorporate them into a new epidemiological model framework which separates these different components of immunity. Our model recapitulates both the current severity of CoV-2 and the relatively benign nature of HCoVs; suggesting that once the endemic phase is reached, CoV-2 may be no more virulent than the common cold. The benign outcome at the endemic phase is contingent on the virus causing primary infections in children. We predict a very different outcome were a CoV like MERS (that causes severe disease in children) to become endemic. These results force us to re-evaluate control measures that rely on identifying and isolating symptomatic infections, and reconsider ideas regarding herd immunity and the use of immune individuals as shields to protect vulnerable groups.

Download Full-text

Projected epidemiological consequences of the Omicron SARS-CoV-2 variant in England, December 2021 to April 2022

10.1101/2021.12.15.21267858 ◽

2021 ◽

Author(s):

Rosanna C Barnard ◽

Nicholas G Davies ◽

Carl A B Pearson ◽

Mark Jit ◽

W John Edmunds

Keyword(s):

Health Outcomes ◽

Compartmental Model ◽

Hospital Admissions ◽

Immune Escape ◽

Epidemiological Data ◽

Control Measures ◽

Gene Target ◽

S Gene ◽

Failure Data ◽

Pharmaceutical Interventions

The Omicron B.1.1.529 SARS-CoV-2 variant was first detected in late November 2021 and has since spread to multiple countries worldwide. We model the potential consequences of the Omicron variant on SARS-CoV-2 transmission and health outcomes in England between December 2021 and April 2022, using a deterministic compartmental model fitted to epidemiological data from March 2020 onwards. Because of uncertainty around the characteristics of Omicron, we explore scenarios varying the extent of Omicron's immune escape and the effectiveness of COVID-19 booster vaccinations against Omicron, assuming the level of Omicron's transmissibility relative to Delta to match the growth in observed S gene target failure data in England. We consider strategies for the re-introduction of control measures in response to projected surges in transmission, as well as scenarios varying the uptake and speed of COVID-19 booster vaccinations and the rate of Omicron's introduction into the population. These results suggest that Omicron has the potential to cause substantial surges in cases, hospital admissions and deaths in populations with high levels of immunity, including England. The reintroduction of additional non-pharmaceutical interventions may be required to prevent hospital admissions exceeding the levels seen in England during the previous peak in winter 2020-2021.

Download Full-text

China’s effective control and other countries’ uncharted challenge against COVID-19: an epidemiological and modelling study

10.1101/2020.04.28.20083899 ◽

2020 ◽

Author(s):

Lingling Zheng ◽

Qin Kang ◽

Weiyao Liao ◽

Xiujuan Chen ◽

Shuai Huang ◽

...

Keyword(s):

Disease Transmission ◽

Reproduction Number ◽

Case Reports ◽

Epidemiological Data ◽

Control Measures ◽

Time Dependent ◽

Effective Control ◽

List Type ◽

Global Epidemic ◽

Serial Interval

AbstractBackgroundOn the present trajectory, COVID is inevitably becoming a global epidemic, leading to concerns regarding the pandemic potential in China and other countries.ObjectiveIn this study, we use the time-dependent reproduction number (Rt) to comprise the COVID transmissibility across different countries.MethodsWe used data from Jan 20, 2019, to Feb 29, 2020, on the number of newly confirmed cases, obtained from the reports published by the CDC, to infer the incidence of infectious over time. A two-step procedure was used to estimate the Rt. The first step used data on known index-secondary cases pairs, from publicly available case reports, to estimate the serial interval distribution. The second step estimated the Rt jointly from the incidence data and the information data in the first step. Rt was then used to simulate the epidemics across all major cities in China and typical countries worldwide.ResultsBased on a total of 126 index-secondary cases pairs from 4 international regions, we estimated that the serial interval for SARS-2-CoV was 4.18 (IQR 1.92 – 6.65) days. Domestically, Rt of China, Hubei province, Wuhan had fallen below 1.0 on 9 Feb, 10 Feb and 13 Feb (Rt were 0.99±0.02, 0.99±0.02 and 0.96±0.02), respectively. Internationally, as of 26 Feb, statistically significant periods of COVID spread (Rt >1) were identified for most regions, except for Singapore (Rt was 0.92±0.17).ConclusionsThe epidemic in China has been well controlled, but the worldwide pandemic has not been well controlled. Worldwide preparedness and vulnerability against COVID-19 should be regarded with more care.What is already known on this subject?The basic reproduction number (R0) and the-time-dependent reproduction number (Rt) are two important indicators of infectious disease transmission. In addition, Rt as a derivative of R0 could be used to assess the epidemiological development of the disease and effectiveness of control measures. Most current researches used data from earlier periods in Wuhan and refer to the epidemiological features of SARS, which are possibly biased. Meanwhile, there are fewer studies discussed the Rt of COVID-19. Current clinical and epidemiological data are insufficient to help us understand the full view of the potential transmission of this disease.What this study adds?We use up-to-data observation of the serial interval and cases arising from local transmission to calculate the Rt in different outbreak level area and every province in China as well as five-top sever outbreak countries and other overseas. By comparing the Rt, we discussed the situation of outbreak around the world.

Download Full-text

The impact of surveillance and control on highly pathogenic avian inuenza outbreaks in poultry in Dhaka division, Bangladesh

10.1101/193177 ◽

2017 ◽

Author(s):

Edward M. Hill ◽

Thomas House ◽

Madhur S. Dhingra ◽

Wantanee Kalpravidh ◽

Subhash Morzaria ◽

...

Keyword(s):

Disease Transmission ◽

Control Measure ◽

Control Policy ◽

Transmission Model ◽

Model Framework ◽

Highly Pathogenic ◽

Management Actions ◽

H5n1 Influenza ◽

And Control ◽

The Impact

AbstractIn Bangladesh, the poultry industry is an economically and socially important sector, but it is persistently threatened by the effects of H5N1 highly pathogenic avian influenza. Thus, identifying the optimal control policy in response to an emerging disease outbreak is a key challenge for policy-makers. To inform this aim, a common approach is to carry out simulation studies comparing plausible strategies, while accounting for known capacity restrictions. In this study we perform simulations of a previously developed H5N1 influenza transmission model framework, fitted to two separate historical outbreaks, to assess specific control objectives related to the burden or duration of H5N1 outbreaks among poultry farms in the Dhaka division of Bangladesh. In particular, we explore the optimal implementation of ring culling, ring vaccination and active surveillance measures when presuming disease transmission predominately occurs from premises-to-premises, versus a setting requiring the inclusion of external factors. Additionally, we determine the sensitivity of the management actions under consideration to differing levels of capacity constraints and outbreaks with disparate transmission dynamics. While we find that reactive culling and vaccination policies should pay close attention to these factors to ensure intervention targeting is optimised, across multiple settings the top performing control action amongst those under consideration were targeted proactive surveillance schemes. Our findings may advise the type of control measure, plus its intensity, that could potentially be applied in the event of a developing outbreak of H5N1 amongst originally H5N1 virus-free commercially-reared poultry in the Dhaka division of Bangladesh.

Download Full-text

Critical fluctuations in epidemic models explain COVID-19 post-lockdown dynamics

Scientific Reports ◽

10.1038/s41598-021-93366-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Maíra Aguiar ◽

Joseba Bidaurrazaga Van-Dierdonck ◽

Javier Mar ◽

Nicole Cusimano ◽

Damián Knopoff ◽

...

Keyword(s):

Disease Transmission ◽

Transmission Rate ◽

Threshold Value ◽

Critical Value ◽

Control Measures ◽

Supercritical Regime ◽

Reproduction Ratio ◽

Critical Fluctuations ◽

Complex Models ◽

Disease Spreading

AbstractAs the COVID-19 pandemic progressed, research on mathematical modeling became imperative and very influential to understand the epidemiological dynamics of disease spreading. The momentary reproduction ratio r(t) of an epidemic is used as a public health guiding tool to evaluate the course of the epidemic, with the evolution of r(t) being the reasoning behind tightening and relaxing control measures over time. Here we investigate critical fluctuations around the epidemiological threshold, resembling new waves, even when the community disease transmission rate $$\beta$$ β is not significantly changing. Without loss of generality, we use simple models that can be treated analytically and results are applied to more complex models describing COVID-19 epidemics. Our analysis shows that, rather than the supercritical regime (infectivity larger than a critical value, $$\beta > \beta _c$$ β > β c ) leading to new exponential growth of infection, the subcritical regime (infectivity smaller than a critical value, $$\beta < \beta _c$$ β < β c ) with small import is able to explain the dynamic behaviour of COVID-19 spreading after a lockdown lifting, with $$r(t) \approx 1$$ r ( t ) ≈ 1 hovering around its threshold value.

Download Full-text

Reconstruction of the transmission dynamics of the first COVID-19 epidemic wave in Thailand

10.21203/rs.3.rs-253746/v1 ◽

2021 ◽

Author(s):

Chaiwat Wilasang ◽

Natcha Jitsuk ◽

Chayanin Sararat ◽

Charin Modchang

Keyword(s):

Disease Transmission ◽

Infected Individual ◽

Mainland China ◽

Stochastic Modelling ◽

Epidemiological Data ◽

Control Measures ◽

Transmission Dynamics ◽

Thai Government ◽

And Control ◽

Epidemic Wave

Abstract Thailand was the first country reporting the first Coronavirus disease 2019 (COVID-19) infected individual outside mainland China. Here we delineated the course of the COVID-19 outbreak together with the timeline of the control measures and public health policies employed by the Thai government during the first wave of the COVID-19 outbreak in Thailand. Based on the comprehensive epidemiological data, we reconstructed the dynamics of COVID-19 transmission in Thailand using a stochastic modelling approach. Our stochastic model incorporated effects of individual heterogeneity in infectiousness on the disease transmission, which allows us to capture relevant features of superspreading events. We found that our model could accurately capture the transmission dynamics of the first COVID-19 epidemic wave in Thailand. The model predicted that at the end of the first wave, the number of cumulative confirmed cases was 3,091 (95%CI: 2,782 - 3,400). We also estimated the time-varying reproduction number (Rt) during the first epidemic wave. We found that after implementing the nationwide interventions, the Rt in Thailand decreased from the peak value of 5.67 to a value below one in less than one month, indicating that the control measures employed by the Thai government during the first COVID-19 epidemic wave were effective. Finally, effects of transmission heterogeneity and control measures on the likelihood of outbreak extinction were also investigated.

Download Full-text

Social encounter networks: collective properties and disease transmission

Journal of The Royal Society Interface ◽

10.1098/rsif.2012.0357 ◽

2012 ◽

Vol 9 (76) ◽

pp. 2826-2833 ◽

Cited By ~ 55

Author(s):

Leon Danon ◽

Thomas A. House ◽

Jonathan M. Read ◽

Matt J. Keeling

Keyword(s):

Network Structure ◽

Disease Transmission ◽

Large Scale ◽

Mechanistic Model ◽

Population Level ◽

Epidemiological Data ◽

Control Measures ◽

Local Network ◽

Form Analysis ◽

Disease Epidemiology

A fundamental challenge of modern infectious disease epidemiology is to quantify the networks of social and physical contacts through which transmission can occur. Understanding the collective properties of these interactions is critical for both accurate prediction of the spread of infection and determining optimal control measures. However, even the basic properties of such networks are poorly quantified, forcing predictions to be made based on strong assumptions concerning network structure. Here, we report on the results of a large-scale survey of social encounters mainly conducted in Great Britain. First, we characterize the distribution of contacts, which possesses a lognormal body and a power-law tail with an exponent of −2.45; we provide a plausible mechanistic model that captures this form. Analysis of the high level of local clustering of contacts reveals additional structure within the network, implying that social contacts are degree assortative. Finally, we describe the epidemiological implications of this local network structure: these contradict the usual predictions from networks with heavy-tailed degree distributions and contain public-health messages about control. Our findings help us to determine the types of realistic network structure that should be assumed in future population level studies of infection transmission, leading to better interpretations of epidemiological data and more appropriate policy decisions.

Download Full-text

Characterizing the effectiveness of COVID-19 mitigation measures: A data-centric approach (Preprint)

10.2196/preprints.21321 ◽

2020 ◽

Author(s):

Lukman Olagoke ◽

Ahmet E. Topcu

Keyword(s):

Control Measure ◽

Control Measures ◽

World Health ◽

Mitigation Measures ◽

Effective Control ◽

The World ◽

Decision Making Processes ◽

And Performance ◽

Health Organization ◽

The Impact

BACKGROUND COVID-19 represents a serious threat to both national health and economic systems. To curb this pandemic, the World Health Organization (WHO) issued a series of COVID-19 public safety guidelines. Different countries around the world initiated different measures in line with the WHO guidelines to mitigate and investigate the spread of COVID-19 in their territories. OBJECTIVE The aim of this paper is to quantitatively evaluate the effectiveness of these control measures using a data-centric approach. METHODS We begin with a simple text analysis of coronavirus-related articles and show that reports on similar outbreaks in the past strongly proposed similar control measures. This reaffirms the fact that these control measures are in order. Subsequently, we propose a simple performance statistic that quantifies general performance and performance under the different measures that were initiated. A density based clustering of based on performance statistic was carried out to group countries based on performance. RESULTS The performance statistic helps evaluate quantitatively the impact of COVID-19 control measures. Countries tend show variability in performance under different control measures. The performance statistic has negative correlation with cases of death which is a useful characteristics for COVID-19 control measure performance analysis. A web-based time-line visualization that enables comparison of performances and cases across continents and subregions is presented. CONCLUSIONS The performance metric is relevant for the analysis of the impact of COVID-19 control measures. This can help caregivers and policymakers identify effective control measures and reduce cases of death due to COVID-19. The interactive web visualizer provides easily digested and quick feedback to augment decision-making processes in the COVID-19 response measures evaluation. CLINICALTRIAL Not Applicable

Download Full-text