Statistical models which represent the impact of strict measures vs no strict measures (and less economic impact), enforced due to Covid-19 I have been wondering whether there are statistical models like this one, (which estimated the deaths caused by the virus itself) that estimate the deaths caused by the economic turndown. There is an estimate of how much the pandemic would cost the Israeli health system, and just a general economic analysis of the impact of the pandemic. I was wondering if there are for example models which show the impact of the strict measures(deaths caused by the economic crisis due to lockdown) and just models which show how much more deaths would occur if there was no strict lockdown (and hence the no economic crisis, or at least not as drastic as after complete nationwide lockdowns) than the economic crisis itself.

I am not particularly interested in any specific country, just examples if such exist.

I'm looking for a model that shows the consequences of lockdowns and its economic impacts. ie how much more/fewer people will die from economic collapse rather than the virus given there was no lockdown (but social distancing and mass testing involved).
"The Benefits and Costs of Using Social Distancing to Flatten the Curve for COVID-19", Thunstrom et al.


  We examine the net benefits of social distancing to slow the spread of COVID-19 in the United States. Social distancing saves lives but imposes large costs on society due to reduced economic activity. We use epidemiological and economic forecasting to perform a rapid benefit-cost analysis of controlling the COVID-19 outbreak. Assuming that social distancing measures can substantially reduce contacts among individuals, we find net benefits of about $5.2 trillion in our benchmark case. We examine the magnitude of the critical parameters that might imply negative net benefits, including the value of statistical life and the discount rate. A key unknown factor is the speed of economic recovery with and without social distancing measures in place. A series of robustness checks also highlight the key role of the value of mortality risk reductions and discounting in the analysis and point to a need for effective economic stimulus when the outbreak has passed.

"A Simple Planning Problem for COVID-19 Lockdown" (NBER) Alvarez, Argente, and Lippi


  We study the optimal lockdown policy for a planner who wants to control the fatalities of a pandemic while minimizing the output costs of the lockdown. We use the SIR epidemiology model and a linear economy to formalize the planner's dynamic control problem. The optimal policy depends on the fraction of infected and susceptible in the population. We parametrize the model using data on the COVID19 pandemic and the economic breadth of the lockdown. The quantitative analysis identifies the features that shape the intensity and duration of the optimal lockdown policy. Our baseline parametrization is conditional on a 1% of infected agents at the outbreak, no cure for the disease, and the possibility of testing. The optimal policy prescribes a severe lockdown beginning two weeks after the outbreak, covers 60% of the population after a month, and is gradually withdrawn covering 20% of the population after 3 months. The intensity of the lockdown depends on the gradient of the fatality rate as a function of the infected, and on the assumed value of a statistical life. The absence of testing increases the economic costs of the lockdown, and shortens the duration of the optimal lockdown which ends more abruptly. Welfare under the optimal policy with testing is higher, equivalent to a one-time payment of 2% of GDP.



Also 


"The Macroeconomics of Epidemics" by Eichenbaum, Rebelo, and Trabandt. Published in NBER in March when it got NYT coverage; there's an "April update" now. The abstract of that paper isn't very revealing (as it only describes one scenario), but they actually consider several scenarios in the paper, with various exogenous constraints like treatments being found effective or not, vaccines being discovered etc. And then they consider a lockdown sequence optimization problem [in each scenario] as a Ramsey problem; see section 5 in the paper... and then some "exit strategies" from each. (Even that can be formulated as an optimization problem, which they call "smart containment" but it requires knowing the immunity status of everyone--which is a bit unrealistic as they admit.) Teaser figure from the paper (I won't try to explain it here):

"Macroeconomic Dynamics and Reallocation in an Epidemic", (NBER, also in CEPR with somewhat more copyediting) Krueger, Uhlig, Xie


  In this paper we argue that endogenous shifts in private consumption behavior across sectors of the economy can act as a potent mitigation mechanism during an epidemic or when the economy is re-opened after a temporary lockdown. Extending the theoretical framework proposed by Eichenbaum-Rebelo-Trabandt (2020), we distinguish goods by their degree to which they can be consumed at home rather than in a social (and thus possibly contagious) context. We demonstrate that, within the model the "Swedish solution" of letting the epidemic play out without government intervention and allowing agents to shift their sectoral behavior on their own can lead to a substantial mitigation of the economic and human costs of the COVID-19 crisis, avoiding more than 80 [percent] of the decline in output and of number of deaths within one year, compared to a model in which sectors are assumed to be homogeneous. For different parameter configurations that capture the additional social distancing and hygiene activities individuals might engage in voluntarily, we show that infections may decline entirely on their own, simply due to the individually rational re-allocation of economic activity: the curve not only just flattens, it gets reversed.

"Internal and External Effects of Social Distancing in a Pandemic" (NBER) Farboodi, Jarosch, Shimer


  We use a conventional dynamic economic model to integrate individual optimization, equilibrium interactions, and policy analysis into the canonical epidemiological model. Our tractable framework allows us to represent both equilibrium and optimal allocations as a set of differential equations that can jointly be solved with the epidemiological model in a unified fashion. Quantitatively, the laissez-faire equilibrium accounts for the decline in social activity we measure in US micro-data from SafeGraph. Relative to that, we highlight three key features of the optimal policy: it imposes immediate, discontinuous social distancing; it keeps social distancing in place for a long time or until treatment is found; and it is never extremely restrictive, keeping the effective reproduction number mildly above the share of the population susceptible to the disease.



Not strictly about Covid-19, but also recent paper from FRB members (and one MIT faculty):


Correia, Luck and Verner. "Pandemics Depress the Economy, Public Health Interventions Do Not: Evidence from the 1918 Flu", 


  what are the economic costs and benefits of non-pharmaceutical interventions (NPI)? Using geographic variation in mortality during the 1918 Flu Pandemic in the U.S., we find that more exposed areas experience a sharp and persistent decline in economic activity. The estimates imply that the pandemic reduced manufacturing output by 18%. The downturn is driven by both supply and demand-side channels. Further, building on findings from the epidemiology literature establishing that NPIs decrease influenza mortality, we use variation in the timing and intensity of NPIs across U.S. cities to study their economic effects. We find that cities that intervened earlier and more aggressively do not perform worse and, if anything, grow faster after the pandemic is over. Our findings thus indicate that NPIs not only lower mortality; they may also mitigate the adverse economic consequences of a pandemic.

A 2nd (recent) paper from a FRB author on the 1918 pandemic, albeit a bit more descriptive: "What Happened to the US Economy During the 1918 Influenza Pandemic? A View Through High-Frequency Data" (and the part most relevant to the question here is left in teaser form in the abstract...)


  Interventions to hinder the contagion were brief (typically a month) and there is some evidence that interventions made a difference for economic outcomes.