The unintended consequences of inconsistent pandemic control policies

Abstract

Controlling the spread of COVID-19 -- even after a licensed vaccine is available -- requires the effective use of non-pharmaceutical interventions, e.g., physical distancing, limits on group sizes, mask wearing, etc.. To date, such interventions have neither been uniformly nor systematically implemented in most countries. For example, even when under strict stay-at-home orders, numerous jurisdictions granted exceptions and/or were in close proximity to locations with entirely different regulations in place. Here, we investigate the impact of such geographic inconsistencies in epidemic control policies by coupling search and mobility data to a simple mathematical model of SARS-COV2 transmission. Our results show that while stay-at-home orders decrease contacts in most areas of the United States of America (US), some specific activities and venues often see an increase in attendance. Indeed, over the month of March 2020, between 10 and 30% of churches in the US saw increases in attendance; even as the total number of visits to churches declined nationally. This heterogeneity, where certain venues see substantial increases in attendance while others close, suggests that closure can cause individuals to find an open venue, even if that requires longer-distance travel. And, indeed, the average distance travelled to churches in the US rose by 13% over the same period. Strikingly, our mathematical model reveals that, across a broad range of model parameters, partial measures can often be worse than no measures at all. In the most severe cases, individuals not complying with policies by traveling to neighboring jurisdictions can create epidemics when the outbreak would otherwise have been controlled. Taken together, our data analysis and modelling results highlight the potential unintended consequences of inconsistent epidemic control policies and stress the importance of balancing the societal needs of a population with the risk of an outbreak growing into a large epidemic.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

“…The first column describes the main categories, the second the sub-categories, the last the country/countries focus of each study and the references. Category Subcategory Country/countries of study and reference Compartmental SIR-like Brazil, China [48] ; China [49] , [50] , [51] ; China, Italy [52] ; China, France, Iran, Italy, South Korea, USA [53] ; France, Iran, Italy [54] ; Germany [55] ; India [56] ; Italy [57] ; USA [58] ; 187 countries [45] ; several European countries [44] , [59] SIR-like age structured China [60] , Italy [61] , 143 countries [47] SEIR-like Argentina, Japan, Indonesia, New Zealand, Spain, USA [62] ; Canada [63] , [64] ; Canada, Germany, Italy [65] ; China [66] , [67] , [68] , [69] ; China, Italy [70] ; China, UK [71] ; Germany [72] ; India [73] , [74] ; Indonesia [75] ; Ireland [76] ; Mexico [77] ; Pakistan [78] ; South Korea [79] , [80] ; Switzerland [81] ; USA [82] , [83] ...…”

“…We start considering the body of modeling work based on SIR-like models. This subset of papers studies the effects of isolation and quarantine policies [49] , models NPIs as effective changes of the parameters [44] , [45] , [48] , [50] , [52] , [54] , [55] , [56] , [57] , [59] , investigates the impact of different policies [53] , [58] , and defines the conditions for the optimal control of the spreading [51] . In the following, I will highlight some of these approaches.…”

Early effects of COVID‐19 on US fisheries and seafood consumption

“…Some areas, particularly in the Southeast and Pacific Northwest have seen some recovery since June 2020 ( Figure S1). This may be due to a combination of state-level differences in initial severity of COVID-19, social distancing restrictions and subsequent reopening strategies (Althouse et al, 2020;Hale et al, 2020;White & Hébert-Dufresne, 2020).…”

“…Each of these sub-sectors will likely be affected differently by the fallout from COVID-19, especially given differences in responses to the pandemic across US states and at a federal level (Althouse et al, 2020;Hale et al, 2020;White & Hébert-Dufresne, 2020;Froehlich et al, 2020).…”

Mask mandates can limit COVID spread: Quantitative assessment of month-over-month effectiveness of governmental policies in reducing the number of new COVID-19 cases in 37 US States and the District of Columbia

“…15 In the absence of clear guidance and direct leadership, variations in MM acceptance and enforcement have arisen within and between US States. 17,[19][20][21][22] Promulgating and exacerbating confusion regarding the effectiveness of wearing face masks, adjudicating the effectiveness of MM orders has largely been left to qualitative interpretations and visual/numerical inspections of the epidemic curves depicting the number of new cases over time. Qualitative assessment cannot objectively test the a priori hypothesis that wearing a mask in public spaces reduces the incidence of COVID-19 cases.…”