The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic

James, Lyndon; Salomon, Joshua A.; Buckee, Caroline O.; Menzies, Nicolas A

doi:10.1177/0272989x21990391

Cited by 111 publications

(75 citation statements)

References 36 publications

(40 reference statements)

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“…perhaps individuals were unaware that cell phone usage could be a risk) [ [20] , [21] ]. In summary, observational results from this DETER data set can be used to inform analyses of infectious disease transmission with epidemiologic models [ [22] , [23] , [24] , [25] ]. Furthermore, results can be used to advance more effective public health policies and guidelines that include cleaning regimens for public environmental objects and the removal or relocation of frequently touched objects to help limit the spread of COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Lee

Laefer

2021

Infection Prevention in Practice

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BACKGROUND Epidemiological studies have long been used to develop infection transmission prevention, but exact patterns of touch behaviours and transportation choices (COVID-19 community spread contributors) were previously unknown. AIM To investigate individual risk behaviour levels with respect to local COVID-19 infection levels. METHODS A longitudinal field study recorded behaviours of individuals leaving medical facilities following the New York State’s PAUSE order. A subset of that data was analyzed herein (4,793 records, 16 facilities, 23 rd March – 17 th May 2020). Touched objects and transportation choices were compared over time using chi-square tests (p < 0.05 significance threshold). FINDINGS Over eight weeks, touching progressively decreased but for-hire vehicle usage increased. In week 1, 60.4% of subjects touched at least one object: a building’s door handle (21.8%); traffic light, railing, or parking meter (5.6%); shared object (19.7%, e.g. vehicle’s door handle); personal object (13.9%, e.g. cell phone); or themselves (0.7%). Certain touch points, however, remained. Throughout the study, public transportation ridership remained steady (about 20%); for-hire car usage increased from 0% in week one to 7% in week eight, mirroring a 7% decrease in personal vehicle usage (from 34% to 27%). Touching and transportation patterns varied significantly by facility. CONCLUSIONS Inconsistent trends in risk related behaviour were documented in the eight weeks following a NYC PAUSE order. Namely, while overall touching decreased 25%, there was no appreciable change in cell phone usage, and for-hire vehicles usage increased 7%.

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Section: Discussionmentioning

confidence: 99%

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Lee

Laefer

2021

Infection Prevention in Practice

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“…Since the beginning of the COVID-19 pandemic a substantial number of works were devoted at its modeling, ranging from SIR and SEIR mean-field models and variants [10] to agent-based models [11] and meta-population based models [12] (for reviews of different forecasting models used for COVID-19 see [13] and [14] ). The main limitation relies in model calibration due to the lack of reliable data resulting from the emergency situation caused by a pandemic [15] , mainly during its initial period, that must be used with care [16] . Despite that, models have a been valuable planning tool in predicting possible future scenarios [5] .…”

Section: Introductionmentioning

confidence: 99%

A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil

Filho

Moret

Chow

et al. 2021

Chaos, Solitons & Fractals

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We introduce a compartmental model SEIAHRV (Susceptible, Exposed, Infected, Asymptomatic, Hospitalized, Recovered, Vaccinated) with age structure for the spread of the SARAS-CoV virus. In order to model current different vaccines we use compartments for individuals vaccinated with one and two doses without vaccine failure and a compartment for vaccinated individual with vaccine failure. The model allows to consider any number of different vaccines with different efficacies and delays between doses. Contacts among age groups are modeled by a contact matrix and the contagion matrix is obtained from a probability of contagion per contact. The model uses known epidemiological parameters and the time dependent probability is obtained by fitting the model output to the series of deaths in each locality, and reflects non-pharmaceutical interventions. As a benchmark the output of the model is compared to two good quality serological surveys, and applied to study the evolution of the COVID-19 pandemic in the main Brazilian cities with a total population of more than one million. We also discuss with some detail the case of the city of Manaus which raised special attention due to a previous report of We also estimate the attack rate, the total proportion of cases (symptomatic and asymptomatic) with respect to the total population, for all Brazilian states since the beginning of the COVID-19 pandemic. We argue that the model present here is relevant to assessing present policies not only in Brazil but also in any place where good serological surveys are not available.

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“…Researchers have called attention to the limited accuracy of models, the lack of transparency and inherent gender biases in data sources, limitations in assumptions and methods, and the danger of using either flawed estimates or a single estimate to inform policy. [16][17][18][19][20][21][22] The politics of GBV data, as well as a preoccupation with quantification for policy attention and resources, may contribute to the motivation underlying GBV modelling. 23 Gender-related issues and GBV have often been excluded from administrative and survey data and have also often been viewed as outside mainstream work on global health and development, leading to very limited data on GBV being available in most countries.…”

Section: Introductionmentioning

confidence: 99%

The pitfalls of modelling the effects of COVID-19 on gender-based violence: lessons learnt and ways forward

et al. 2021

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Since early 2020, global stakeholders have highlighted the significant gendered consequences of the COVID-19 pandemic, including increases in the risk of gender-based violence (GBV). Researchers have sought to inform the pandemic response through a diverse set of methodologies, including early efforts modelling anticipated increases in GBV. For example, in April 2020, a highly cited modelling effort by the United Nations Population Fund (UNFPA) and partners projected headline global figures of 31 million additional cases of intimate partner violence due to 6 months of lockdown, and an additional 13 million child marriages by 2030. In this paper, we discuss the rationale for using modelling to make projections about GBV, and use the projections released by UNFPA to draw attention to the assumptions and biases underlying model-based projections. We raise five key critiques: (1) reducing complex issues to simplified, linear cause-effect relationships, (2) reliance on a small number of studies to generate global estimates, (3) assuming that the pandemic results in the complete service disruption for existing interventions, (4) lack of clarity in indicators used and sources of estimates, and (5) failure to account for margins of uncertainty. We argue that there is a need to consider the motivations and consequences of using modelling data as a planning tool for complex issues like GBV, and conclude by suggesting key considerations for policymakers and practitioners in using and commissioning such projections.

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The Use and Misuse of Mathematical Modeling for Infectious Disease Policymaking: Lessons for the COVID-19 Pandemic

Cited by 111 publications

References 36 publications

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

Spring 2020 COVID-19 community transmission behaviours around New York City medical facilities

A data-driven model for COVID-19 pandemic – Evolution of the attack rate and prognosis for Brazil

The pitfalls of modelling the effects of COVID-19 on gender-based violence: lessons learnt and ways forward

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