scholarly journals On the fallibility of simulation models in informing pandemic responses

2020 ◽  
Author(s):  
Deepti Gurdasani ◽  
Hisham Ziauddeen
Keyword(s):  
Simulation Models ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Parameter Estimates ◽  
Case Detection ◽  
Rapid Testing ◽  
Real World Data ◽  
Effective Strategies ◽  
Reliable Parameter

In the early stages of pandemics, mathematical models can provide invaluable insights into transmission dynamics, help predict disease spread, and evaluate control measures. However models are only valid within the limits of the parameters examined. As reliable parameter estimates are rarely available early in a new pandemic, best-guess estimates are used, which need to be constantly reviewed as new real-world data emerge. Estimating how sensitive the model is to changes in its parameters can provide useful information about validity when parameters are uncertain. Interpreting models without considering these factors can lead to flawed inferences, which can have far reaching effects when they inform public health policy. We illustrate this, here, using an example from the Hellewell et al. model published in Lancet Global Health, 2020. This model suggested that case detection and contact tracing was unlikely to be an effective strategy for pandemic control, and is likely to have informed UK government strategy to cease testing and contact tracing on the 12th March 2020. We show that this model is very sensitive to the parameter of delay between case detection and isolation. We demonstrate that when the delay scenario parameter is changed to a median of 1 day, which is very plausible in the context of current rapid testing, this model predicts a >80% probability of controlling the epidemic within 12 weeks, with relatively modest contact tracing. These results suggest that rapid testing, contact tracing and isolation could be effective strategies to control transmission.

scholarly journals Investigating the effectiveness of re-opening policies before vaccination during a pandemic: SD modelling research based on COVID-19 in Wuhan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ying Qian ◽  
Wei Xie ◽  
Jidi Zhao ◽  
Ming Xue ◽  
Shiyong Liu ◽  
...  
Keyword(s):  
Model Simulation ◽  
Control Measures ◽  
World Health ◽  
Contact Tracing ◽  
Infection Prevention And Control ◽  
Real World Data ◽  
Medical Resources ◽  
Health Organization ◽  
The Impact ◽  
Different Levels

Abstract Background Lockdown policies were widely adopted during the coronavirus disease 2019 (COVID-19) pandemic to control the spread of the virus before vaccines became available. These policies had significant economic impacts and caused social disruptions. Early re-opening is preferable, but it introduces the risk of a resurgence of the epidemic. Although the World Health Organization has outlined criteria for re-opening, decisions on re-opening are mainly based on epidemiologic criteria. To date, the effectiveness of re-opening policies remains unclear. Methods A system dynamics COVID-19 model, SEIHR(Q), was constructed by integrating infection prevention and control measures implemented in Wuhan into the classic SEIR epidemiological model and was validated with real-world data. The input data were obtained from official websites and the published literature. Results The simulation results showed that track-and-trace measures had significant effects on the level of risk associated with re-opening. In the case of Wuhan, where comprehensive contact tracing was implemented, there would have been almost no risk associated with re-opening. With partial contact tracing, re-opening would have led to a minor second wave of the epidemic. However, if only limited contact tracing had been implemented, a more severe second outbreak of the epidemic would have occurred, overwhelming the available medical resources. If the ability to implement a track-trace-quarantine policy is fixed, the epidemiological criteria need to be further taken into account. The model simulation revealed different levels of risk associated with re-opening under different levels of track-and-trace ability and various epidemiological criteria. A matrix was developed to evaluate the effectiveness of the re-opening policies. Conclusions The SEIHR(Q) model designed in this study can quantify the impact of various re-opening policies on the spread of COVID-19. Integrating epidemiologic criteria, the contact tracing policy, and medical resources, the model simulation predicts whether the re-opening policy is likely to lead to a further outbreak of the epidemic and provides evidence-based support for decisions regarding safe re-opening during an ongoing epidemic. Keyords COVID-19; Risk of re-opening; Effectiveness of re-opening policies; IPC measures; SD modelling.

scholarly journals Reducing spread of COVID-19 in closed environments: an outbreak investigation and modelling study in dormitory settings

2020 ◽  
Author(s):  
Rachael Pung ◽  
Bernard Lin ◽  
Sebastian Maurer-Stroh ◽  
Fernanda L Sirota ◽  
Tze Minn Mak ◽  
...  
Keyword(s):  
Attack Rate ◽  
Simulation Models ◽  
Housing Condition ◽  
Control Measures ◽  
Disease Spread ◽  
Medical Attention ◽  
Acute Infections ◽  
Current Outbreak ◽  
Living Environments ◽  
Close Contacts

Abstract Starting with a handful of SARS-CoV-2 infections in dormitory residents in late March 2020, rapid tranmission in their dense living environments ensued and by October 2020, more than 50,000 acute infections were identified across various dormitories. Extensive epidemiological, serological and phylogentic investigations, supported by simulation models, helped to reveal the factors of transmission and impact of control measures in a dormitory. We find that asymptomatic cases and symptomatic cases who did not seek medical attention were major drivers of the outbreak. Furthermore, each resident has about 30 close contacts and each infected resident spread to 4.4 (IQR 3.5–5.3) others at the start of the outbreak. The final attack rate of the current outbreak was 76.2% (IQR 70.6%–98.0%) and could be reduced by further 10% under a modified dormitory housing condition. These findings are important when designing living environments in a post COVID-19 future to reduce disease spread and facilitate rapid implementation of outbreak control measures.

scholarly journals Factors influencing SARS-CoV-2 transmission and outbreak control measures in densely populated settings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rachael Pung ◽  
Bernard Lin ◽  
Sebastian Maurer-Stroh ◽  
Fernanda L. Sirota ◽  
Tze Minn Mak ◽  
...  
Keyword(s):  
Simulation Models ◽  
Control Measures ◽  
Disease Spread ◽  
Medical Attention ◽  
Outbreak Control ◽  
Acute Infections ◽  
Rapid Transmission ◽  
Current Outbreak ◽  
Living Environments ◽  
The Impact

AbstractStarting with a handful of SARS-CoV-2 infections in dormitory residents in late March 2020, rapid transmission in their dense living environments ensued and by October 2020, more than 50,000 acute infections were identified across various dormitories in Singapore. The aim of the study is to identify combination of factors facilitating SARS-CoV-2 transmission and the impact of control measures in a dormitory through extensive epidemiological, serological and phylogenetic investigations, supported by simulation models. Our findings showed that asymptomatic cases and symptomatic cases who did not seek medical attention were major drivers of the outbreak. Furthermore, each resident had about 30 close contacts and each infected resident spread to 4.4 (IQR 3.5–5.3) others at the start of the outbreak. The final attack rate of the current outbreak was 76.2% (IQR 70.6–98.0%) and could be reduced by further 10% under a modified dormitory housing condition. These findings are important when designing living environments in a post COVID-19 future to reduce disease spread and facilitate rapid implementation of outbreak control measures.

scholarly journals Transmission Dynamics of SARS-CoV-2 during an Outbreak in a Roma Community in Thessaly, Greece—Control Measures and Lessons Learned

2021 ◽  
Vol 18 (6) ◽  
pp. 2878
Author(s):  
Michalis Koureas ◽  
Matthaios Speletas ◽  
Zacharoula Bogogiannidou ◽  
Dimitris Babalis ◽  
Vassilios Pinakas ◽  
...  
Keyword(s):  
Univariate Analysis ◽  
Lessons Learned ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Chi Square ◽  
Increased Risk ◽  
Hospital Visits ◽  
Index Cases ◽  
Isolation Facility

A COVID-19 outbreak occurred among residents of a Roma settlement in Greece (8 April–4 June 2020). The aim of this study was to identify factors associated with an increased risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and to evaluate the effectiveness of control measures implemented. Data were analyzed from individuals that were tested for SARS-CoV-2 during contact tracing, population screening or hospital visits. RT-PCR was used for the detection of SARS-CoV-2 in oropharyngeal samples. Risk factors for household secondary attack rates (SAR) and hospitalization with COVID-19 were examined using chi-square tests, Fisher’s exact tests and logistic regression analyses. During the outbreak, 142 cases, 20 hospitalizations and 1 death were recorded, with a total of 2273 individuals tested. The risk of hospitalization was associated with age (OR: 1.04, 95% CI: 1.02–1.07) and Cycle threshold (Ct) values (OR for a decrease in Ct values by 1: 1.18, 95% CI: 1.07–1.31). Household SAR was estimated at 38.62% (95% CI: 32.50–45.01%). After the designation of an isolation facility for cases, household SAR declined from 74.42% to 31.03%. Household size was associated with the risk of infection (OR: 2.65, 95% CI: 1.00–7.07). The presence of COVID-19 symptoms among index cases was correlated with higher transmission (OR: 23.68, 95% CI 2.21–253.74) in multivariate analysis, while age was found to be associated with SAR only in univariate analysis. Roma communities can be particularly vulnerable to the spread of SARS-CoV-2. In similar settings, symptomatic cases are more important transmitters of SARS-CoV-2. Within these communities, immediate measures should be implemented to mitigate disease spread.

scholarly journals Real-world trajectory sharing with local differential privacy

2021 ◽  
Vol 14 (11) ◽  
pp. 2283-2295
Author(s):  
Teddy Cunningham ◽  
Graham Cormode ◽  
Hakan Ferhatosmanoglu ◽  
Divesh Srivastava
Keyword(s):  
Real World ◽  
Differential Privacy ◽  
Alternative Methods ◽  
Disease Spread ◽  
Contact Tracing ◽  
Trajectory Data ◽  
Real World Data ◽  
Or Efficiency ◽  
Real World Application ◽  
Public Data

Sharing trajectories is beneficial for many real-world applications, such as managing disease spread through contact tracing and tailoring public services to a population's travel patterns. However, public concern over privacy and data protection has limited the extent to which this data is shared. Local differential privacy enables data sharing in which users share a perturbed version of their data, but existing mechanisms fail to incorporate user-independent public knowledge (e.g., business locations and opening times, public transport schedules, geo-located tweets). This limitation makes mechanisms too restrictive, gives unrealistic outputs, and ultimately leads to low practical utility. To address these concerns, we propose a local differentially private mechanism that is based on perturbing hierarchically-structured, overlapping n -grams (i.e., contiguous subsequences of length n ) of trajectory data. Our mechanism uses a multi-dimensional hierarchy over publicly available external knowledge of real-world places of interest to improve the realism and utility of the perturbed, shared trajectories. Importantly, including real-world public data does not negatively affect privacy or efficiency. Our experiments, using real-world data and a range of queries, each with real-world application analogues, demonstrate the superiority of our approach over a range of alternative methods.

scholarly journals Reopening universities during the COVID-19 pandemic: A testing strategy to minimize active cases and delay outbreaks

2020 ◽  
Author(s):  
Lior Rennert ◽  
Corey A. Kalbaugh ◽  
Lu Shi ◽  
Christopher McMahan
Keyword(s):  
Mitigation Strategies ◽  
Disease Spread ◽  
Contact Tracing ◽  
Infectious Period ◽  
University Campuses ◽  
Effective Strategies ◽  
Viral Spread ◽  
Extreme Risk ◽  
The Impact ◽  
Maximum Occupancy

AbstractBackgroundUniversity campuses present an ideal environment for viral spread and are therefore at extreme risk of serving as a hotbed for a COVID-19 outbreak. While active surveillance throughout the semester such as widespread testing, contact tracing, and case isolation, may assist in detecting and preventing early outbreaks, these strategies will not be sufficient should a larger outbreak occur. It is therefore necessary to limit the initial number of active cases at the start of the semester. We examine the impact of pre-semester NAT testing on disease spread in a university setting.MethodsWe implement simple dynamic transmission models of SARS-CoV-2 infection to explore the effects of pre-semester testing strategies on the number of active infections and occupied isolation beds throughout the semester. We assume an infectious period of 3 days and vary R0 to represent the effectiveness of disease mitigation strategies throughout the semester. We assume the prevalence of active cases at the beginning of the semester is 5%. The sensitivity of the NAT test is set at 90%.ResultsIf no pre-semester screening is mandated, the peak number of active infections occurs in under 10 days and the size of the peak is substantial, ranging from 5,000 active infections when effective mitigation strategies (R0 = 1.25) are implemented to over 15,000 active infections for less effective strategies (R0 = 3). When one NAT test is mandated within one week of campus arrival, effective (R0 = 1.25) and less effective (R0 = 3) mitigation strategies delay the onset of the peak to 40 days and 17 days, respectively, and result in peak size ranging from 1,000 to over 15,000 active infections. When two NAT tests are mandated, effective (R0 = 1.25) and less effective (R0 = 3) mitigation strategies delay the onset of the peak through the end of fall semester and 20 days, respectively, and result in peak size ranging from less than 1,000 to over 15,000 active infections. If maximum occupancy of isolation beds is set to 2% of the student population, then isolation beds would only be available for a range of 1 in 2 confirmed cases (R0 = 1.25) to 1 in 40 confirmed cases (R0 = 3) before maximum occupancy is reached.ConclusionEven with highly effective mitigation strategies throughout the semester, inadequate pre-semester testing will lead to early and large surges of the disease and result in universities quickly reaching their isolation bed capacity. We therefore recommend NAT testing within one week of campus return. While this strategy is sufficient for delaying the timing of the outbreak, pre-semester testing would need to be implemented in conjunction with effective mitigation strategies to reduce the outbreak size.

scholarly journals Investigation of the Efficiency of Mask Wearing, Contact Tracing, and Case Isolation during the COVID-19 Outbreak

2021 ◽  
Vol 10 (13) ◽  
pp. 2761
Author(s):  
Tatiana Filonets ◽  
Maxim Solovchuk ◽  
Wayne Gao ◽  
Tony Wen-Hann Sheu
Keyword(s):  
Mathematical Model ◽  
Reproduction Number ◽  
Control Measures ◽  
Disease Spread ◽  
Contact Tracing ◽  
Imported Cases ◽  
Quantitative Results ◽  
High Level ◽  
Pharmaceutical Interventions ◽  
The Basic Reproduction Number

Case isolation and contact tracing are two essential parts of control measures to prevent the spread of COVID-19, however, additional interventions, such as mask wearing, are required. Taiwan successfully contained local COVID-19 transmission after the initial imported cases in the country in early 2020 after applying the above-mentioned interventions. In order to explain the containment of the disease spread in Taiwan and understand the efficiency of different non-pharmaceutical interventions, a mathematical model has been developed. A stochastic model was implemented in order to estimate the effectiveness of mask wearing together with case isolation and contact tracing. We investigated different approaches towards mask usage, estimated the effect of the interventions on the basic reproduction number (R0), and simulated the possibility of controlling the outbreak. With the assumption that non-medical and medical masks have 20% and 50% efficiency, respectively, case isolation works on 100%, 70% of all people wear medical masks, and R0 = 2.5, there is almost 80% probability of outbreak control with 60% contact tracing, whereas for non-medical masks the highest probability is only about 20%. With a large proportion of infectiousness before the onset of symptoms (40%) and the presence of asymptomatic cases, the investigated interventions (isolation of cases, contact tracing, and mask wearing by all people), implemented on a high level, can help to control the disease spread. Superspreading events have also been included in our model in order to estimate their impact on the outbreak and to understand how restrictions on gathering and social distancing can help to control the outbreak. The obtained quantitative results are in agreement with the empirical COVID-19 data in Taiwan.

An Epidemiological Analysis of COVID-19 Confirmed Cases, Reported at National Institute of Health, Pakistan (Preprint)

2020 ◽  
Author(s):  
Ambreen Chaudhry
Keyword(s):  
Age Groups ◽  
Target Population ◽  
Control Measures ◽  
Contact Tracing ◽  
Bivariate Analysis ◽  
Travel History ◽  
Disease Epidemiology ◽  
Hygiene Practices ◽  
Multi Center Study ◽  
Epidemiological Characteristics

BACKGROUND Coronavirus disease (Covid-19) is a zoonotic disease of novel origin that posed a continuous threat to health worldwide after taking the shape of the pandemic. An understanding of disease epidemiology is supportive in timely preventive and control measures as well as contact tracing and curbing surveillance activities. OBJECTIVE The objective of our study was to determine the epidemiological characteristics of COVID-19 confirmed cases reported at the National Institute of Health Pakistan and elements of its spread in Pakistan. METHODS A retrospective record review was conducted at the National Institute of Health (NIH) Islamabad, Pakistan from January 25 to April 4, 2020. Univariate and bivariate analysis was done with 95% CI and p<0.05. RESULTS A total of 14,422 samples of suspected COVID-19 cases were received with a positivity rate of 9% (n=1348). Among all 70% (n=939) were male. The median age was 41years of age (range: 01-99Years). Among all, 19% were from 30-39 years old followed by 50-59 years old (17%). Children remained the least affected by 3% (n=35). Of the total reported cases, 55% (n=735) have reported the travel history within the last 14 days. Among these travelers’ international travelers were 23% (n=166) and domestic travelers were 77% (n=569). Travel history including both international and domestic remained significantly associated with the different age groups and Young adults remained more vulnerable to COVID-19 (P=0.03). Fever, SOB, and Cough remained the most significantly associated (P<0.05) in all age groups. CONCLUSIONS A higher incidence of COVID-19 among elderly men suggests robust quarantine measures for this target population. An escalating incidence of local transmission needs strict social distancing and hygiene practices to help flatten the curve. An extensive multi-center study is also recommended for a full understanding of disease dynamics.

scholarly journals Role of efficient testing and contact tracing in mitigating the COVID-19 pandemic: a network modelling study

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e045886
Author(s):  
Yiying Hu ◽  
Jianying Guo ◽  
Guanqiao Li ◽  
Xi Lu ◽  
Xiang Li ◽  
...  
Keyword(s):  
Real World ◽  
Case Fatality ◽  
Contact Tracing ◽  
Secondary Outcome ◽  
Time Interval ◽  
Fatality Rate ◽  
Real World Data ◽  
Network Modelling ◽  
World Data ◽  
Quantitative Evidence

ObjectivesThis study quantified how the efficiency of testing and contact tracing impacts the spread of COVID-19. The average time interval between infection and quarantine, whether asymptomatic cases are tested or not, and initial delays to beginning a testing and tracing programme were investigated.SettingWe developed a novel individual-level network model, called CoTECT (Testing Efficiency and Contact Tracing model for COVID-19), using key parameters from recent studies to quantify the impacts of testing and tracing efficiency. The model distinguishes infection from confirmation by integrating a ‘T’ compartment, which represents infections confirmed by testing and quarantine. The compartments of presymptomatic (E), asymptomatic (I), symptomatic (Is), and death with (F) or without (f) test confirmation were also included in the model. Three scenarios were evaluated in a closed population of 3000 individuals to mimic community-level dynamics. Real-world data from four Nordic countries were also analysed.Primary and secondary outcome measuresSimulation result: total/peak daily infections and confirmed cases, total deaths (confirmed/unconfirmed by testing), fatalities and the case fatality rate. Real-world analysis: confirmed cases and deaths per million people.Results(1) Shortening the duration between Is and T from 12 to 4 days reduces infections by 85.2% and deaths by 88.8%. (2) Testing and tracing regardless of symptoms reduce infections by 35.7% and deaths by 46.2% compared with testing only symptomatic cases. (3) Reducing the delay to implementing a testing and tracing programme from 50 to 10 days reduces infections by 35.2% and deaths by 44.6%. These results were robust to sensitivity analysis. An analysis of real-world data showed that tests per case early in the pandemic are critical for reducing confirmed cases and the fatality rate.ConclusionsReducing testing delays will help to contain outbreaks. These results provide policymakers with quantitative evidence of efficiency as a critical value in developing testing and contact tracing strategies.

scholarly journals How detection ranges and usage stops impact digital contact tracing effectiveness for COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Konstantin D. Pandl ◽  
Scott Thiebes ◽  
Manuel Schmidt-Kraepelin ◽  
Ali Sunyaev
Keyword(s):  
Disease Spread ◽  
Contact Tracing ◽  
Behavioral Characteristics ◽  
Policy Makers ◽  
Detection Range ◽  
Proximity Detection ◽  
Effective Technology ◽  
Different Types ◽  
Effectiveness And Efficiency ◽  
The Impact

AbstractTo combat the COVID-19 pandemic, many countries around the globe have adopted digital contact tracing apps. Various technologies exist to trace contacts that are potentially prone to different types of tracing errors. Here, we study the impact of different proximity detection ranges on the effectiveness and efficiency of digital contact tracing apps. Furthermore, we study a usage stop effect induced by a false positive quarantine. Our results reveal that policy makers should adjust digital contact tracing apps to the behavioral characteristics of a society. Based on this, the proximity detection range should at least cover the range of a disease spread, and be much wider in certain cases. The widely used Bluetooth Low Energy protocol may not necessarily be the most effective technology for contact tracing.

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