Non-pharmaceutical interventions (NPIs) are the cornerstone of infectious disease outbreak response in the absence of effective pharmaceutical interventions. Outbreak strategies often involve combinations of NPIs that may change according to disease prevalence and population response. Little is known with regard to how costly each NPI is to implement. This information is essential to inform policy decisions for outbreak response.
To address this gap in existing literature, we conducted a systematic review on outbreak costings and simulation studies related to a number of NPI strategies, including isolating infected individuals, contact tracing and quarantine, and school closures.
Our search covered the MEDLINE and EMBASE databases, studies published between 1990 and 24 March 2020 were included. We included studies containing cost data for our NPIs of interest in pandemic, epidemic, and outbreak response scenarios.
We identified 61 relevant studies. There was substantial heterogeneity in the cost components recorded for NPIs in outbreak costing studies. The direct costs of NPIs for which costing studies existed also ranged widely: isolating infected individuals per case: US$141.18 to US$1042.68 (2020 values), tracing and quarantine of contacts per contact: US$40.73 to US$93.59, social distancing: US$33.76 to US$167.92, personal protection and hygiene: US$0.15 to US$895.60.
While there are gaps and heterogeneity in available cost data, the findings of this review and the collated cost database serve as an important resource for evidence-based decision-making for estimating costs pertaining to NPI implementation in future outbreak response policies.
There are gaps in existing non-pharmaceutical intervention cost data literature both geographically and by intervention.
Publishing costs for the SARS-CoV-2 pandemic outbreak responses will help fill these gaps.
The SARS-CoV-2 pandemic has put unprecedented strain on health systems around the world and brought to the fore the importance of establishing effective infectious disease outbreak response strategies to protect population health. Countries have had to implement non-pharmaceutical interventions (NPIs) in the absence of suitable vaccines and other medical interventions as part of their outbreak mitigation or suppression strategies . NPIs often come with a direct and socioeconomic cost, as in addition to administration costs or lost wages, they often require changes in behavioural patterns, which in turn, have wider impacts such as productivity losses or reduced consumption.
Considering that NPIs have been adopted at scale by nearly all countries globally as a response to SARS-CoV-2 in 2020, and for prolonged periods of time, discussion regarding the burden brought by the costs associated with NPIs has become commonplace . Countries were making decisions on suppression and mitigation strategies early on in the pandemic while ignoring the costs associated with these interventions when implemented on a large scale. As the costs and scale of interruption associated with the SARS-CoV-2 pandemic and control interventions are becoming apparent, the current pandemic also acts as a prompt to consider the costs of NPIs associated with outbreak response strategies generally. Knowing the costs of NPIs would help countries to make informed evidence-based decisions when deciding on NPIs for future outbreaks, leading to more resilient health systems. This being said, NPI costs remain relevant for SARS-CoV-2 as although vaccines are being rolled out, it will likely still be many months before populations are vaccinated at a level that would allow for NPIs to be completely lifted around the globe.
Previous literature reviews on NPIs have focussed on particular pathogens or NPIs. Examples of such reviews include school closures for influenza pandemics, or travel bans, [3, 4]. To our knowledge, a comprehensive systematic review covering all the literature on costs for all settings and pathogens for community-based NPIs does not yet exist. There is a great need for this review, as we need to map what is known about the costs of these community-based NPIs for different settings and for different pathogens so that knowledge gaps can be identified and filled to improve the evidence available, and to inform future strategies relating to outbreak response in cases where pharmaceutical interventions are not available or feasible.
The aim of this review is to provide a comprehensive overview of the existing literature on the costs of community-based NPIs. We cover the costs of NPIs relating to isolating infected individuals, contact tracing and quarantine, travel and flight restrictions, social distancing, point-of-entry measures, and personal protection and hygiene in relation to outbreaks in non-hospital settings. We include studies that are both presenting outbreak response costs as well as simulation studies.
The objective of this literature review was to capture the literature on costs of community-based NPIs for different types of outbreak settings. Studies of interest were separated into two categories: outbreak costing studies, and simulation studies. We define outbreak costing studies as studies which contain observed primary costs for components of NPI implementation in outbreak response scenarios, which could be used in economic models and future policy decisions. Simulation studies, on the other hand, are more useful for identifying relevant literature on applying different NPI modelling strategies, or for policy-making purposes where comparative costs between different strategies are considered.
2.1 Inclusion and Exclusion Criteria
Table 1 presents the inclusion and exclusion criteria for our review. We considered outbreaks affecting the human population (excluding outbreaks in animals) in any location published from 1990 onwards for any non-chronic infectious disease. We only included original articles or reviews published or accepted in a peer-reviewed journal or published reports from official public health bodies, such as the Centers for Disease Control, published in English. We focused on interventions in the community, as these are most likely to provide useful information to inform response strategies for larger outbreaks, such as SARS-CoV-2. Studies involving hospital employees were included if the hospital was within a community outbreak (e.g., costs of home isolation of infected healthcare workers during community-wide H1N1 influenza outbreak), otherwise we excluded hospital-based studies as we deemed them to not be representative of a general community outbreak scenario. Studies for which pharmaceutical intervention costs could not be separated from non-pharmaceutical intervention costs were excluded.
2.2 Non-pharmaceutical Interventions of Interest
We considered NPIs that related to isolating infectious individuals or contacts, or included community interventions aiming to reduce community contacts through social distancing, such as curfews, school closures, workplace contact reductions (through closure, workplace or school absenteeism, or remote working), and wider crowd avoidance measures such as avoiding public transport and events. We also included stricter community-wide social distancing interventions, such as community-wide or country-wide stay-at-home orders. Additionally, we included travel restrictions and border closures and measures at points of entry, focussing on scans or screens done when individuals are entering or exiting a country or region. For personal protection measures, we included community-based usage of face masks, gloves, hand hygiene measures, and sanitisation protocols of contaminated surfaces. Table 2 presents a full list of NPIs considered.
2.3 Intervention Costs of Interest
For outbreak costing studies, we extracted costs incurred by the individual affected by the NPI (e.g., wages lost due to home quarantine), costs incurred by the government, business, or public health body due to administering the NPI (e.g., contact tracing activities, face masks), and information relating to labour (e.g., number of hours spent on contact investigation per contact). We did not extract costs that were linked to pharmaceutical interventions that were combined with an NPI (e.g., vaccine administration costs) or case management in hospitals. For simulation studies, we included studies which presented costs separately from pharmaceutical costs. We covered simulation studies presenting any kind of financial impact, from cost calculations to reductions in gross domestic product.
For the quarantine of infectious individuals and their contacts, we considered cost or labour data relating to quarantine in a non-hospital setting. We excluded the costs of quarantine in hospital settings, as we considered them to not be representative of the costs relating to a community-based quarantine intervention due to the additional costs of components such as medical staff and hospital beds. We included costs relating to testing for infection only if testing was a component of the case identification and contact tracing protocol. With regard to contact tracing, we were interested in the community investigation costs and not pharmaceutical intervention costs. This meant that studies which did not separate non-pharmaceutical contact investigation costs from the vaccine or prophylactic treatment costs were excluded.
All costs from the outbreak-costing studies were converted to 2020 USD (mid-year, June) by first inflating the cost in its original reported currency to 2020 and then converting the value to USD . The initial consumer price index was matched to the month when the intervention occurred, or the mid-point of the intervention timing if it lasted for a longer time-frame. The method of inflation adjustment followed the following formula:
The outputs of simulation studies were not converted as they are often the outcome of multiple inputs and assumptions, meaning that converting their outcomes would not be appropriate.
2.4 Search Strategy
We searched the MEDLINE and EMBASE databases for studies pertaining to the NPIs described in Table 1 on 24 March 2020. The search strategy, including the search strings, can be found in the supplement file called “Search strategy”. The two databases were chosen, as they are the major databases that cover literature on pandemics, epidemics, and outbreaks, leading us to believe that other databases would have likely only added duplicate references.
The literature review was conducted systematically, meaning that at both title and abstract screening and full text screening, each paper was examined by two reviewers of the review team, these included ABH, ALS, HAS, JS, JWEO, LC, LD, MX, and SSW. Conflicts were resolved in conflict resolution meetings between two members of the review team (JS, JWEO). We followed a first-degree snowball approach for the relevant reviews identified in our screening process, where studies in the identified review were evaluated for inclusion, but second-degree references (references of references) were not. We enquired about full texts of difficult-to-find studies through the British Library.
We adapted the British Medical Journal guidelines for assessing economic studies . Our quality assessment contained 26 points, some of which were exclusive only to simulation studies. We categorised studies as low, medium, or high quality based on the proportion of “Yes” scores to the total number of points that were applicable to the study. Studies of low quality covered 25 % or fewer of the points, studies of moderate quality covered between > 25 % and < 75 % of the points, while studies of high quality covered ≥ 75 % of the points. See supplementary spreadsheet for individual quality assessment scores for each study.
We registered the literature review on PROSPERO (review ID CRD42020177418).
3.1 Studies Identified
We identified 4599 studies for title and abstract screening, 4359 of which were excluded and 121 studies were assessed for eligibility during full-text screening. Additionally, nine reviews were reference checked. Consequently, we identified a total of 61 relevant studies with cost information on relevant NPIs (27 costing studies and 34 simulation studies). Of these 61 studies, 4 were identified through reference-checking reviews relevant to the NPIs of interest, while the remaining 57 were identified directly through the MEDLINE and EMBASE search (see Fig. 1). At the full-text screening phase, there was disagreement between reviewers regarding inclusion for 27 (22.3%) studies. Of the included studies, 1.6% (1/61) were assessed as being of low quality, 44.2% (27/61) were assessed as being of moderate quality, and 54.1% (33/61) were assessed as being of high quality (see supplementary spreadsheet for full quality assessment for each study).
In the following sections, we present the identified cost and simulation evidence for each category of NPI (see Fig. 2 for number of studies by intervention). Due to the heterogeneity of costs recorded for the implementation strategies, it was deemed inappropriate to pool cost estimates. Hence, here we present the range of costs identified when there are comparable intervention components.
3.2 Non-hospital Isolation of Infected Individuals
We identified 11 outbreak costing studies relating to isolating infected individuals at home or in a hotel in outbreak scenarios [9,10,11,12,13,14,15,16,17,18,19], and three simulation studies that explored the costs of isolating infected individuals [20,21,22]. Table 3 summarises the available cost information from these studies in 2020 USD (US$) converted to unit costs where applicable for the outbreak costing studies, and Table 4 summarises the simulation studies in the reported currencies (see supplementary spreadsheet for original extracted data in its original currencies and units). The available studies were focused largely in Europe, North America, and China, with few studies from low- and middle-income countries (LMICs). The pathogens were vaccine-preventable diseases (measles, pertussis), diarrhoeal pathogens (norovirus, Escherichia coli), or respiratory pathogens (H1N1 influenza, SARS).
The costs covered by the 11 studies were highly heterogeneous, and included case confirmation costs, wages and productivity lost due to being in quarantine, costs of taking care of quarantined children at home. One study considered the costs incurred to the government due to isolating infected individuals during the SARS pandemic response in Singapore, and reported the costs of quarantine enforcement (US$340.23 [2020 values] per case), quarantine command centres (US$71.63 per case), quarantine allowance (US$322.32 per case), and emergency call centres (US$71.63 per case) . There was one cost component, laboratory costs relating to case confirmation, that was covered by multiple studies. The ranges of laboratory costs are presented in section 3.8. The three simulation studies presented heterogeneous cost-related outputs, including the total cost of isolating infectious individuals, cost effectiveness of an isolation intervention versus vaccination, and cost effectiveness of isolating infectious individuals given different levels of contact tracing.
3.3 Tracing and Quarantine of Contacts
We identified nine cost studies [11, 14, 15, 17, 23,24,25,26,27], and four simulation studies relating to contact tracing and contact quarantine in outbreak scenarios [22, 36,37,38]. Tables 3 and 4 summarise the cost information from these studies (the original extracted data in original currencies and units can be found in the supplementary spreadsheet). The studies were focussed on respiratory diseases (SARS and influenza) and vaccine-preventable diseases (measles and mumps). Much the same as isolation of infected individuals, the identified contact tracing papers were from North America and China.
As with case isolation, there was substantial heterogeneity in the types of costs recorded by the outbreak costing studies. Ranges of costs relating to laboratory testing are presented in section 3.8. The average hours spent on contact tracing was reported by five studies on measles outbreaks, and ranged from 0.5 to 11.9 hours [11, 14, 23, 26, 27]. The four simulation studies presented costs of contact tracing and quarantine at home and in a hotel.
3.4 Travel and Flight Bans
We did not identify any outbreak costing studies on travel and flight bans or restrictions. However, we did identify three simulation studies [39,40,41], see Table 4 for further details and original extracted costs in the supplementary spreadsheet. All studies were on influenza, two were located in the USA and one in New Zealand. The two USA studies simulated the costs and GDP impacts of air travel restrictions, while the New Zealand study covered the full border closure.
3.5 Social Distancing
We identified five costing [28,29,30,31,32] and 25 simulation studies on social distancing measures [40, 42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65], see Tables 3 and 4, respectively. Again, studies largely focussed on North America and Europe. All studies on a specified disease were on respiratory infections (various strains of influenza).
All costing studies reported only on school closures, and presented heterogenous costs, including days of work lost by parents, income loss due to lost work, and cost of childcare due to school closure. The simulation studies largely focussed on school closures and workplace absenteeism or closure, with many studies also considering combinations of community contact-reducing interventions.
3.6 Measures for Persons at Point-of-entry
We identified one simulation study on NPI measures at point-of-entry . This USA-based study simulated the costs per airline passenger of point-of-entry screening for Ebola for three different monitoring levels (Table 4).
3.7 Personal Protection and Hygiene
While personal protection and hygiene measures in hospital settings for hospital-based outbreaks and nosocomial transmission were well documented, studies involving community-based outbreaks or community usage were rarer (see Tables 3 and 4 for costing and simulation studies, respectively). We identified four costing [11, 33,34,35] and three simulation studies on personal protection and hygiene measures [33, 46, 67]. The countries covered were USA, China and Zimbabwe. Most studies were on influenza, with one on measles and another on cholera. Face masks and hand sanitiser were the most covered interventions.
Three costing studies reported the costs of N95 face masks, which ranged from US$0.28 to US$2.14 [33,34,35]. Three simulation studies covered the savings due to different N95 face mask usage levels, and costs of general hygiene and hand hygiene measures.
3.8 Laboratory Testing in Conjunction with Non-pharmaceutical Interventions
We included only studies where laboratory testing was combined with another NPI. We identified 11 costing studies that involved laboratory cost data, 4 of which were related to isolation of infectious cases [9, 14, 18, 19] and 7 were related to contact tracing (Table 3) [11, 15, 17, 23,24,25, 27]. We also identified one simulation study on laboratory testing in conjunction with an NPI, which was a cost-benefit analysis of an E. coli surveillance system in Colorado, USA (Table 4) .
The diseases covered were vaccine-preventable (measles, mumps, pertussis, hepatitis A), respiratory (H1N1), and E. coli. The only pathogen for which there were costs reported for more than one study was measles, where six studies contained information [11, 14, 18, 23, 25, 27]. For the measles studies, the reported costs of testing ranged from US$25.88 to US$641.00 per sample and data on hours ranged from 0.5 to 101.9 hours per sample. The reporting of components included in laboratory cost calculations were not consistent, as some studies reported cost of labour as part of laboratory costs and others did not.
In this study, we have reviewed the existing published literature on the NPIs of interest, covering both outbreak costing studies, which contain primary costs relating to NPIs in outbreak response, and simulation studies, which estimate costs of NPIs in outbreak response. Cost data are essential components of any evidence-based policy process and provide valuable information to be used alongside evidence of effectiveness to inform analyses pertaining to projected or actual estimates of the cost effectiveness and budget impact of implementation of different NPI strategies. There is variability in the levels of representation amongst the different NPI categories. Case isolation, contact tracing measures, and social distancing measures (in particular school closures) were well represented while travel restrictions, point-of-entry measures, and personal hygiene measures were less represented. Wider and stricter social distancing measures, such as community-wide measures, had not been covered in published literature before March 2020. Labour costs were often the most expensive component of isolating infected individuals and contact tracing, while laboratory costs also contributed greatly to the overall cost. There were nine papers that included NPIs and their costs, but did not present these costs separately from pharmaceutical (often vaccines and/or antivirals) interventions, and as such were excluded as the costs of the two different types of intervention could not be separated.
While we identified multiple costing studies that contained cost information for NPIs, providing meaningful and comparable summary statistics for them is difficult, as studies covered multiple locations and recorded different cost components relating to the community-based NPIs. Having a database of available cost information from outbreak costing studies is nonetheless useful for ease of locating relevant studies and cost components in future applications, such as for model parameterisation or in scenarios where policy-makers must compare the costs of different potential interventions. Studies covering the costs of travel bans and measures at point-of-entry would be a valuable addition to the existing literature. As many countries closed their borders or restricted entry into the country in the first months of 2020, this knowledge gap in cost data may be covered to an extent in literature that has been published since then . The simulation studies also provided a range of model outputs, ranging from the total cost of implementing an NPI to the estimated impact on a country’s GDP. The database of simulation studies can act as a starting point for estimating the costs of a community-based NPI during an outbreak.
Published literature on the costs of NPIs for outbreaks in low-income settings was sparse. The majority of the studies identified were focused on North America, Europe, or Australia and New Zealand. While this may, in part, be by the exclusion of non-English studies and grey literature, this alone is likely not the only reason for the trend. In order to make well-informed pandemic response decisions, it is important that costings studies focus on low-income settings. The ongoing SARS-CoV-2 pandemic offers an opportunity for countries to collect outbreak response cost data for low-income settings to help fill this knowledge gap. We found that many studies were excluded from this review because they did not disentangle NPI costs from pharmaceutical intervention costs. It would be helpful if studies would present these costs separately to provide a clearer view of how each intervention contributes to the total cost of outbreak response.
The results published in this study are limited by the scope and extent of the literature review. This review covered literature that had been published by 24 March 2020. This necessarily limits the identification of publications to only those published up to the very beginnings of the SARS-CoV-2 pandemic. We did not identify any studies that recorded costs or simulated costs of strict social distancing measures (i.e., community stay-at-home orders) that are now commonplace across the globe for controlling the SARS-CoV-2 pandemic, due to this early cut-off point, which is a limitation of this study. This study does provide a broad review of the available epidemic- and pandemic-related research until COVID-19, and future research relating to COVID-19 outbreak costing and simulation studies can build on it. Extensive future research is indeed warranted to capture the cost of implementing NPIs, including strict social distancing, in relation to this unprecedented and devastating outbreak [70,71,72]. This review only covered studies from the MEDLINE and EMBASE databases, which publish studies on outbreaks. Studies that might have been exclusively available in the grey literature would not have been identified in this study.
This review presents the existing literature pertaining to the direct costs of implementing NPIs. There are important additional socioeconomic costs associated with the implementation of NPIs, such as the cost of businesses closing due to the intervention or the effects the NPIs have on mental health, the literature for which has not been covered by this review. Additionally, this review does not comprehensively summarise the cost effectiveness of all possible NPIs in outbreak response. Furthermore, as this review is focussed on the costs of public health measures, the costs of policies such as stimulus packages are beyond the scope of this review. The results of this study can be used for information purposes to provide a narrative summary of the cost of implementing historical NPI strategies, and to inform conversations around future planning for implementation of NPIs for pandemic response. The results of this study are also highly useful to inform future research, where numerous gaps or incomplete data were identified.
During the SARS-CoV-2 pandemic, community-based NPIs such as community-wide social distancing measures have been applied rapidly in countries across the globe, with little evidence available for estimating the costs of such an intervention a priori. Having easily accessible collated cost information on community-based NPI strategies will provide a valuable resource for informing future outbreak response policies, where cost data represent a vital component of any cost-effectiveness assessment of NPI options under consideration for implementation. Literature in this field will likely continue to accrue rapidly over the following months. Additional care should also be taken to collect and publish costs for low-income settings for future planning of pandemic financing. Maintaining a database summarising published literature on NPI costs in relation to outbreak response could be valuable for model parameterisation and outbreak response planning purposes.
Imai N, Gaythorpe KAM, Abbott S, Bhatia S, van Elsland S, Prem K, et al. Adoption and impact of non-pharmaceutical interventions for COVID-19. Wellcome Open Res. 2020;5:59.
Demirguc-Kunt A, Lokshin M, Torre I. The sooner, the better: the early economic impact of non-pharmaceutical interventions during the COVID-19 Pandemic. 2020. https://papers.ssrn.com/abstract=3611386. Accessed 7 Oct 2020.
Bin Nafisah S, Alamery AH, Al Nafesa A, Aleid B, Brazanji NA. School closure during novel influenza: a systematic review. J Infect Public Health. 2018;11:657–61.
Errett NA, Sauer LM, Rutkow L. An integrative review of the limited evidence on international travel bans as an emerging infectious disease disaster control measure. Am J Disaster Med. 2019;14:193–200.
Turner HC, Lauer JA, Tran BX, Teerawattananon Y, Jit M. Adjusting for inflation and currency changes within health economic studies. Value Health. 2019;22:1026–32.
International Monetary Fund. Country Indexes and Weight. 2020. https://data.imf.org/regular.aspx?key=61015892. Accessed 23 Sep 2020.
Bloomberg. Currencies. 2020. https://www.bloomberg.com/markets/currencies. Accessed 23 Sep 2020
Drummond MF, Jefferson TO. Guidelines for authors and peer reviewers of economic submissions to the BMJ. The BMJ Economic Evaluation Working Party. BMJ. 1996;313:275–83.
Christie CD, Glover AM, Willke MJ, Marx ML, Reising SF, Hutchinson NM. Containment of pertussis in the regional pediatric hospital during the Greater Cincinnati epidemic of 1993. Infect Control Hosp Epidemiol. 1995;16:556–63.
Wahl E, Vold L, Lindstedt BA, Bruheim T, Afset JE. Investigation of an Escherichia coli O145 outbreak in a child day-care centre—extensive sampling and characterization of eae- and stx 1-positive E. coli yields epidemiological and socioeconomic insight. BMC Infect Dis. 2011. https://doi.org/10.1186/1471-2334-11-238.
Ma R, Lu L, Suo L, Li X, Yang F, Zhou T, et al. An expensive adult measles outbreak and response in office buildings during the era of accelerated measles elimination, Beijing, China. Vaccine. 2017;35:1117–23.
Galante M, Garin O, Sicuri E, Cots F, García-Altés A, Ferrer M, et al. Health services utilization, work absenteeism and costs of pandemic influenza A (H1N1) 2009 in Spain: a multicenter-longitudinal study. PLoS ONE. 2012;7:e31696.
Mota NVVP, Lobo RD, Toscano CM, Pedrosode Lima AC, Souza Dias MB, Komagata H, et al. Cost-effectiveness of sick leave policies for health care workers with influenza-like illness, Brazil, 2009. Emerg Infect Dis. 2011;17:1421–9.
Sugerman DE, Barskey AE, Delea MG, Ortega-Sanchez IR, Bi D, Ralston KJ, et al. Measles outbreak in a highly vaccinated population, San Diego, 2008: role of the intentionally undervaccinated. Pediatrics. 2010;125:747–55.
Gallagher L, Soyemi K, Conover C, Austin C, Saathoff-Huber L, Nelson S, et al. Outbreak of Escherichia coli O157:H7 in a child care center in Cook County, Illinois, with prolonged shedding and household transmission. Am J Infection Control. 2013. https://doi.org/10.1016/j.ajic.2013.03.312.
Ooi PL, Lim S, Chew SK. Use of quarantine in the control of SARS in Singapore. Am J Infect Control. 2005;33:252–7.
Wang B, Xie J, Fang P. Is a mass prevention and control program for pandemic (H1N1) 2009 good value for money? Evidence from the Chinese Experience. Iran J Public Health. 2012;41:34–43.
Coleman MS, Garbat-Welch L, Burke H, Weinberg M, Humbaugh K, Tindall A, et al. Direct costs of a single case of refugee-imported measles in Kentucky. Vaccine. 2012;30:317–21.
Bownds L, Lindekugel R, Stepak P. Economic impact of a hepatitis A epidemic in a mid-sized urban community: the case of Spokane, Washington. J Commun Health. 2003;28:233–46.
Agusto FB. Optimal isolation control strategies and cost-effectiveness analysis of a two-strain avian influenza model. Biosystems. 2013;113:155–64.
Yarmand H, Ivy JS, Roberts SD, Bengtson MW, Bengtson NM. Cost-effectiveness analysis of vaccination and self-isolation in case of H1N1. In: Proceedings of the 2010 Winter Simulation Conference. 2010. pp. 2199–210.
Mubayi A, Zaleta CK, Martcheva M, Castillo-Chávez C. A cost-based comparison of quarantine strategies for new emerging diseases. Math Biosci Eng. 2010;7:687–717.
Parker AA, Staggs W, Dayan GH, Ortega-Sánchez IR, Rota PA, Lowe L, et al. Implications of a 2005 measles outbreak in Indiana for sustained elimination of measles in the United States. N Engl J Med. 2006;355:447–55.
Pike J, Marin M, Guo A, Haselow D, Safi H, Zhou F. 2016–2017 Arkansas mumps outbreak in a close-knit community: assessment of the economic impact and response strategies. Vaccine. 2020;38:1481–5.
Rosen JB, Arciuolo RJ, Khawja AM, Fu J, Giancotti FR, Zucker JR. Public health consequences of a 2013 measles outbreak in New York City. JAMA Pediatr. 2018;172:811–7.
Dayan GH, Ortega-Sánchez IR, LeBaron CW, Quinlisk MP. Iowa Measles Response Team. The cost of containing one case of measles: the economic impact on the public health infrastructure–Iowa, 2004. Pediatrics. 2005;116:e1-4.
Flego KL, Belshaw DA, Sheppeard V, Weston KM. Impacts of a measles outbreak in Western Sydney on public health resources. Commun Dis Intell Q Rep. 2013;37:E240–5.
Borse RH, Behravesh CB, Dumanovsky T, Zucker JR, Swerdlow D, Edelson P, et al. Closing schools in response to the 2009 pandemic influenza A H1N1 virus in New York City: economic impact on households. Clin Infect Dis. 2011;52(Suppl 1):S168–72.
Chen W-C, Huang AS, Chuang J-H, Chiu C-C, Kuo H-S. Social and economic impact of school closure resulting from pandemic influenza A/H1N1. J Infect. 2011;62:200–3.
Gift TL, Palekar RS, Sodha SV, Kent CK, Fagan RP, Archer WR, et al. Household effects of school closure during pandemic (H1N1) 2009, Pennsylvania, USA. Emerg Infect Dis. 2010;16:1315–7.
Johnson AJ, Moore ZS, Edelson PJ, Kinnane L, Davies M, Shay DK, et al. Household responses to school closure resulting from outbreak of influenza B, North Carolina. Emerg Infect Dis. 2008;14:1024.
Russell ES, Zheteyeva Y, Gao H, Shi J, Rainey JJ, Thoroughman D, et al. Reactive school closure during increased influenza-like illness (ILI) activity in Western Kentucky, 2013: a field evaluation of effect on ILI incidence and economic and social consequences for families. Open Forum Infectious Diseases. Oxford University Press; 2016. https://academic.oup.com/ofid/article-abstract/3/3/ofw113/2593258. Accessed 25 Nov 2020.
Tracht SM, Del Valle SY, Edwards BK. Economic analysis of the use of facemasks during pandemic (H1N1) 2009. J Theor Biol. 2012;300:161–72.
Mukerji S, MacIntyre CR, Seale H, Wang Q, Yang P, Wang X, et al. Cost-effectiveness analysis of N95 respirators and medical masks to protect healthcare workers in China from respiratory infections. BMC Infect Dis. 2017;17:464.
Baracco G, Eisert S, Eagan A, Radonovich L. Comparative cost of stockpiling various types of respiratory protective devices to protect the health care workforce during an influenza pandemic. Disaster Med Public Health Prep. 2015;9:313–8.
Li X, Geng W, Tian H, Lai D. Was Mandatory Quarantine Necessary in China for Controlling the 2009 H1N1 Pandemic? Int J Environ Res Public Health. 2013. https://doi.org/10.3390/ijerph10104690.
Orset C. People’s perception and cost-effectiveness of home confinement during an influenza pandemic: evidence from the French case. Eur J Health Econ. 2018;19:1335–50.
Gupta AG, Moyer CA, Stern DT. The economic impact of quarantine: SARS in Toronto as a case study. J Infect. 2005;50:386–93.
Epstein JM, Goedecke DM, Yu F, Morris RJ, Wagener DK, Bobashev GV. Controlling pandemic flu: the value of international air travel restrictions. PLoS ONE. 2007;2:e401.
Prager F, Wei D, Rose A. Total economic consequences of an influenza outbreak in the United States. Risk Anal. 2017;37:4–19.
Boyd M, Baker MG, Mansoor OD, Kvizhinadze G, Wilson N. Protecting an island nation from extreme pandemic threats: proof-of-concept around border closure as an intervention. PLoS ONE. 2017;12:e0178732.
Andradóttir S, Chiu W, Goldsman D, Lee ML, Tsui K-L, Sander B, et al. Reactive strategies for containing developing outbreaks of pandemic influenza. BMC Public Health. 2011;11(Suppl 1):S1.
Araz OM, Damien P, Paltiel DA, Burke S, van de Geijn B, Galvani A, et al. Simulating school closure policies for cost effective pandemic decision making. BMC Public Health. 2012;12:449.
Brown ST, Tai JHY, Bailey RR, Cooley PC, Wheaton WD, Potter MA, et al. Would school closure for the 2009 H1N1 influenza epidemic have been worth the cost?: a computational simulation of Pennsylvania. BMC Public Health. 2011;11:353.
Halder N, Kelso JK, Milne GJ. Cost-effective strategies for mitigating a future influenza pandemic with H1N1 2009 characteristics. PLoS ONE. 2011;6:e22087.
Jones RM, Adida E. Selecting nonpharmaceutical interventions for influenza. Risk Anal. 2013;33:1473–88.
Kelso JK, Halder N, Postma MJ, Milne GJ. Economic analysis of pandemic influenza mitigation strategies for five pandemic severity categories. BMC Public Health. 2013;13:211.
Keogh-Brown MR, Smith RD, Edmunds JW, Beutels P. The macroeconomic impact of pandemic influenza: estimates from models of the United Kingdom, France, Belgium and The Netherlands. Eur J Health Econ. 2010;11:543–54.
Lempel H, Epstein JM, Hammond RA. Economic cost and health care workforce effects of school closures in the US. PLoS Curr. 2009;1:RRN1051.
Maharaj S, Kleczkowski A. Controlling epidemic spread by social distancing: Do it well or not at all. BMC Public Health. 2012;12:679.
Milne GJ, Halder N, Kelso JK. The cost effectiveness of pandemic influenza interventions: a pandemic severity based analysis. PLoS ONE. 2013;8:e61504.
Morin BR, Perrings C, Levin S, Kinzig A. Disease risk mitigation: the equivalence of two selective mixing strategies on aggregate contact patterns and resulting epidemic spread. J Theor Biol. 2014;363:262–70.
Nishiura H, Ejima K, Mizumoto K, Nakaoka S, Inaba H, Imoto S, et al. Cost-effective length and timing of school closure during an influenza pandemic depend on the severity. Theor Biol Med Model. 2014;11:5.
Perlroth DJ, Glass RJ, Davey VJ, Cannon D, Garber AM, Owens DK. Health outcomes and costs of community mitigation strategies for an influenza pandemic in the United States. Clin Infect Dis. 2010;50:165–74.
Reluga TC. Game theory of social distancing in response to an epidemic. PLoS Comput Biol. 2010. https://doi.org/10.1371/journal.pcbi.1000793.
Sadique MZ, Adams EJ, Edmunds WJ. Estimating the costs of school closure for mitigating an influenza pandemic. BMC Public Health. 2008;8:135.
Sander B, Nizam A, Garrison LP Jr, Postma MJ, Halloran ME, Longini IM Jr. Economic evaluation of influenza pandemic mitigation strategies in the United States using a stochastic microsimulation transmission model. Value Health. 2009;12:226–33.
Saunders-Hastings P, Quinn Hayes B, Smith R, Krewski D. Modelling community-control strategies to protect hospital resources during an influenza pandemic in Ottawa, Canada. PLoS ONE. 2017;12:e0179315.
Smith RD, Keogh-Brown MR. Macroeconomic impact of pandemic influenza and associated policies in Thailand, South Africa and Uganda. Influenza Other Respir Viruses. 2013;7:64–71.
Smith RD, Keogh-Brown MR, Barnett T. Estimating the economic impact of pandemic influenza: an application of the computable general equilibrium model to the UK. Soc Sci Med. 2011. https://doi.org/10.1016/j.socscimed.2011.05.025.
Smith RD, Keogh-Brown MR, Barnett T, Tait J. The economy-wide impact of pandemic influenza on the UK: a computable general equilibrium modelling experiment. BMJ. 2009;339:b4571.
Wang Z, Szeto KY, Leung FC-C. Effectiveness of closure of public places with time delay in disease control. J Integr Bioinform. 2008. https://doi.org/10.2390/biecoll-jib-2008-96.
Wong ZS-Y, Goldsman D, Tsui K-L. Economic evaluation of individual school closure strategies: the Hong Kong 209 H1N1 pandemic. PLoS ONE. 2016;11:e0147052.
Xue Y, Kristiansen IS, de Blasio BF. Dynamic modelling of costs and health consequences of school closure during an influenza pandemic. BMC Public Health. 2012;12:962.
Yaesoubi R, Cohen T. Identifying cost-effective dynamic policies to control epidemics. Stat Med. 2016. https://doi.org/10.1002/sim.7047.
Jacobson SH, Yu G, Jokela JA. A double-risk monitoring and movement restriction policy for Ebola entry screening at airports in the United States. Prev Med. 2016;88:33–8.
Sardar T, Mukhopadhyay S, Bhowmick AR, Chattopadhyay J. An optimal cost effectiveness study on Zimbabwe Cholera Seasonal Data from 2008–2011. PLoS ONE. 2013;8:e81231.
Elbasha E. Costs and benefits of a subtype-specific surveillance system for identifying Escherichia coli O157:H7 outbreaks. Emerging Infectious Dis. 2000. https://doi.org/10.3201/eid0603.000310.
Cheng C, Barceló J, Hartnett AS, Kubinec R, Messerschmidt L. COVID-19 Government Response Event Dataset (CoronaNet v.1.0). Nat Hum Behav. 2020;4:756–68.
Eilersen A, Sneppen K. Estimating cost-benefit of quarantine length for COVID-19 mitigation. Epidemiology. medRxiv; 2020. https://www.medrxiv.org/content/10.1101/2020.04.09.20059790v2?rss=1
Wang Q, Shi N, Huang J, Cui T, Yang L, Ai J, et al. Effectiveness and cost-effectiveness of public health measures to control COVID-19: a modelling study. Epidemiology. medRxiv; 2020. https://www.medrxiv.org/content/10.1101/2020.03.20.20039644v2. Accessed 25 Nov 2020.
Ugarov A. Inclusive Costs of NPI Measures for COVID-19 Pandemic: Three Approaches. Health Economics. MedRxiv; 2020. https://www.medrxiv.org/content/10.1101/2020.03.26.20044552v1. Accessed 25 Nov 2020.
We would like to thank Dr Kris Murray and Dr Laura Anselmi for their suggestions for improvement for the written presentation of this review.
The authors acknowledge funding from The Wellcome Trust, and from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union; and acknowledges funding by Community Jameel. ABH acknowledges support from her Imperial College Research Fellowship. KH was additionally supported by the NIHR HPRU in Modelling and Health Economics, a partnership between PHE, Imperial College London and LSHTM (grant code NIHR200908) JWEO acknowledges support from his MRC Skill development fellowship (reference MR/T025409/1). JS acknowledges PhD funding from the Wellcome Trust (reference 215163/Z/18/Z).
Conflicts of interest
ABH reports personal fees from the World Health Organization, outside the submitted work. ABH was previously engaged by Pfizer Inc to advise on modelling RSV vaccination strategies for which she received no financial compensation, outside the submitted work. ALS, HAS, JS, JWEO, KH, LC, LD, MX, and SSW report no conflicts of interest.
Availability of data and material
Data collected for this literature review is included in the Supplementary spreadsheet that accompanies this manuscript.
Title and abstract screening, full-text screening, preliminary data extraction: JS, LD, JWEO, LC, ABH, ALS, SSW, HAS, MX. Search string testing and planning: JS, JWEO. Data extraction protocol planning, data extraction check and finalisation, data quality assessment, manuscript writing: JS. Comments and edits on manuscript: LD, KH, SWEO, LC, ABH, ALS, SSW, HAS, MX.
About this article
Cite this article
Skarp, J.E., Downey, L.E., Ohrnberger, J.W.E. et al. A Systematic Review of the Costs Relating to Non-pharmaceutical Interventions Against Infectious Disease Outbreaks. Appl Health Econ Health Policy 19, 673–697 (2021). https://doi.org/10.1007/s40258-021-00659-z