Resolve to Save Lives

Public Health & Social Measures Simulator

Using evidence to turn measures on and off

Today, the most effective way to slow the spread of the COVID-19 is to implement public health and social measures (or PHSMs)—including physical distancing and improved hygiene practices.

But PHSMs can cause devastating social and economic disruption if they are not managed carefully.

These decisions are not easy. This simulator allows users to explore how the timing of different combinations of PHSMs could affect disease spread. This simulator is not a forecasting tool to predict new cases, it rather uses the best available assumptions about the impact of different PHSMs for policymakers to anticipate transmission scenarios and how they may change with the different PHSM interventions.

Choose a region to change graphs:

Although this is not a forecasting tool, we use UN Population Data (2020 medium variant estimates) for geographic regions to inform the age structures of our models. Case fatality might differ by regions, depending on the age structure. COVID-19 is known to be more severe among older persons

Shelter in place policies delay a surge in cases and buy time to implement stronger disease control tactics and policies. If that time is not used effectively to implement testing and contact tracing, a second wave of infections is possible

Experience shows that if PHSMs are implemented early and decisively, they can drastically reduce the severity of an epidemic by limiting shortages of hospital beds and other supplies, and ultimately saving lives.These reductions can only be achieved if time bought with early interventions is used to scale up disease control activities. Otherwise a second wave of infections is possible especially  if PHSMs are abruptly lifted.

Use the slider to change the start date and end date of a “shelter in place” policy

Early implementation delays the onset of the first wave of cases, whereas later implementation can flatten the curve

As public health and social measure are lifted, it is critical to scale up testing, isolation, contact tracing, and quarantine to Box In the virus and effectively control transmission

PHSMs are more effective when implemented in combination, or “stacked,” than when implemented individually. The effectiveness of a response also depends on how engaged communities are and how fully they can adopt and continue adhering to PHSMs over time, and other variables such as family size and level of intergenerational contact within a community. Policymakers should only discontinue one or two PHSMs at a time, monitoring the impact on the number of people infected and health system capacity for at least two weeks before additional PHSMs are discontinued. Removal of PHSMs should begin with “expansive” measures (e.g., shelter in place) most detrimental to the community, followed by less disruptive measures (e.g., cancellation of mass gatherings).

The physical distancing measures policy makers have taken to suppress the pandemic have been hugely disruptive, and populations look forward to resuming economic and social activity. Authorities must reopen cautiously, which can be done by “Boxing it In.” This strategy to expand testing, isolate infected people, identify contacts, and quarantine contacts should be rolled out and scaled up while transmission is ongoing, so that physical distancing measures can be safely lifted. All four components of the strategy are crucial; if any one measure is lacking, the virus can escape and spread explosively again. Boxing it In requires urgent expansion of countries’ public health capacity. Our model shows that shelter in place strategies can be lifted with minimal increases in new cases if testing, isolation, contact tracing and quarantine are successfully implemented.

Use the slider to compare the effectiveness of physical distancing measures with “Boxing it In”


Nearly every country in the world had confirmed cases of COVID-19, with more than nine million cases reported globally. While many countries introduced PHSMs quickly, they face a crisis that will extend over years, and must act in ways they can sustain for the long haul. PHSM implementation must coincide with scale up of testing, isolation, contact tracing and quarantine in order control transmission when they are eventually lifted. Ultimately, choosing an optimal set of policies means finding a balance: measuring the rapidly evolving impact of the virus, adapting effective preventive measures to local needs and capacities, and mitigating the measures’ most adverse effects on individuals, households, and communities.


  1. While caseloads remain low, build public health capacity to test, trace isolate, and and treat cases–the necessary foundation for reopening society.
  2. Monitor data on how PHSMs meet local COVD-19 conditions and needs, and to determine when and how to lift them in away that balances lives and livelihoods
  3. Engage communities to adapt PHSMs to the local context and effectively communicate about risk to sustain public support, achieve widespread adherence, and shield vulnerable populations.


This Susceptible-Exposed-Infectious-Recovered (SEIR) model adopts the age structure of the elected geographic region using 2020 U.N. population data. Because COVID-19 is known to be more severe among older persons, the calculated case fatality rate will differ accordingly. This is not a forecasting tool. Detailed information on the methods informing this model are available at and a technical visualization is available at


We thank Dan Hammer and Ed Boyda of Earthrise Media for collaboration on the model


This simulator is not intended to provide an accurate forecast of cases, but rather to illustrate the impacts of policies on transmission scenarios. ‘Infections’ in the model refer to actual COVID-19 infections, rather than observed or documented infections. Evidence for the effectiveness of public health and social measures is evolving rapidly. This is a living document, and we will update the model as more robust evidence arises. Please contact for questions about the model, or if you would like to share recent data to inform parameter selection. The simulator also does not account for a lag between policy implementation and reductions in transmission. The model, including parameters and assumptions, is available at:

Tooltips and Labels:

School Closures: Community acceptance may be stronger if alternative services for childcare and student learning and provision of nutrition are established. We used reductions in contacts by age groups in our age-structured model to replicate the findings of Litinova et al. (2019)
School closure and management practices during coronavirus outbreaks including COVID-19: a rapid systematic review (The Lancet)

Cancellation of Mass Gatherings: Engage with community and religious leaders to articulate value-based decisions and encourage local adoption. Adaptation of existing events, including outdoor services or services in shifts, may be helpful in localities where cancellation of gatherings is not practical. We assumed a reduction in transmission by 28%
The effect of public health measures on the 1918 influenza pandemic in U.S. cities (PNAS)

Shielding the Elderly: While there is a limited evidence base for a “shielding” strategy, such an approach might be of benefit to certain at-risk groups for severe outcomes. We assumed a reduction in transmission among the elderly cohort and their contacts of 50%
Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand (Imperial College)

Isolation and Quarantine: requires the effective testing and isolation and quarantine of contacts. Users should determine whether a household quarantine strategy will be adopted (lower resource intensity; we assume 37% reduction in transmission) or an extended contact tracing strategy and quarantine of all extended contacts will be used (higher resource intensity; we assume 52% reduction in transmission)
Effectiveness of isolation, testing, contact tracing and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study (CMMID)

X-Axis: Days from start of local outbreak