Minimal and adaptive numerical strategy for critical resource planning in a pandemic

Prakash, Meher K.; Kaushal, Shaurya; Bhattacharya, S.; Chandran, Akshay; Kumar, Aloke; Ansumali, Santosh

doi:10.1103/physreve.102.021301

Cited by 10 publications

(8 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work we use the NPIs and R t ’s with the resolution of a week [15]. Each NPI policy code was assumed to be the same for an entire week starting from Monday to Sunday.…”

Section: Resultsmentioning

confidence: 99%

“…This can be a surrogate for the general mobility and associated economic activity in the regions -Travel restrictions: All travel from outside Switzerland is restricted (0), Travel by road to the cantons having a road-entry to Schengen countries is opened (1), In addition to the road travel from Schengen countries, International travel is permitted with a quarantine requirement upon arrival (2) In this work we use the NPIs and Rt's with the resolution of a week [15]. Each NPI policy code was assumed to be the same for an entire week starting from Monday to Sunday.…”

Section: Determinants -Npi Policy Datamentioning

confidence: 99%

See 1 more Smart Citation

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Sruthi

Biswal

Saraswat

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 to September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

show abstract

“…In this work we use the NPIs and R t ’s with the resolution of a week [15]. Each NPI policy code was assumed to be the same for an entire week starting from Monday to Sunday.…”

Section: Resultsmentioning

confidence: 99%

Section: Determinants -Npi Policy Datamentioning

confidence: 99%

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Sruthi

Biswal

Saraswat

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The daily new infection data was obtained from Worldometer [ 15 ]. We work with the assumption that the function is piece-wise continuous [ 13 ], with the resolution of a week [ 12 ]. The average transmission rate for the week was calculated, α t =<1/I active .…”

Section: Resultsmentioning

confidence: 99%

“…Predicting the rates requires training a mathematical or computational model on sufficiently large and systematized data that contains the information on the policies and their consequences. In the initial days of the pandemic, one could find universalities in the rise of infections across the different countries [ 11, 12 ]. However, as the policies and the trajectories diverged, this ability to compare or combine data from across the different countries had been lost.…”

Section: Introductionmentioning

confidence: 99%

Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies

Sruthi

Biswal

Saraswat

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Several questions resonate as the governments relax their COVID-19 mitigation policies - is it too early to relax them, were the policies as effective as they could have been. Answering these questions about the past or crafting newer policy decisions in the future requires a quantification of how policy choices affect the spread of the infection. Policy landscape as well as the infection trajectories from different states and countries diverged so fast that comparing and learning from them has not been easy. In this work, we standardize and pool together the ensemble of lockdown and graded re-opening policies adopted by the 50 states of USA in any given week between 9th March and 9th August. Using artificial intelligence (AI) on this pooled data, we build a predictive model (R2training=0.79, R2test=0.76) for the weekly-averaged transmission rate of infections. Predictability conceptually raises the possibility of an evidence-based or data-driven mitigation policy-making by evaluating the relative merits of the different policy scenarios. Probing the predictions with interpretable AI highlights how factors such as the closing of bars or the use of masks influence transmission, effects which have been hard to decouple from the ensemble of policy instrument combinations. While acknowledging the limitations of our predictions as well as of the infection testing, we ask the theoretical question if the observed transmission rates in the states were as efficient as they could have been under various levels of restrictions, and if the mitigation policies of the states are overdesigned. The model can be further refined with a more detailed inclusion of geographies and policy compliances, as well as expanded as newer policies emerge.

show abstract

“…Even in the simplest scenario of the exponential growth with of infections I with time, we found that choosing a linear fit between dI/dt and I led to a better parameter estimation than an exponential fit between I and t , although both are mathematically equivalent. [8] The parameter estimation scheme we evolved[7] for all the parameters thus relies on rearranging the underlying equations in a way that allowed us to provide most reliable estimates despite the uncertainties and noise in the data. The time-varying epidemiological parameters were estimated for the different districts.…”

Section: Methodsmentioning

confidence: 99%

A steady trickle-down from metro districts and improving epidemic-parameters characterized the increasing COVID-19 cases in India

Ansumali

Kumar

Agrawal

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Background. By mid-September of 2020, the number of daily new infections in India have crossed 95,000. To facilitate an intuition for the spatio-temporal development of the pandemic and to help resource deployment planning, we analyze and describe how the disease burden almost-predictably shifted from large metropolitan districts to sub-urban districts. Methods. We gathered the publicly available granular data from 186 different districts (equivalent of counties) on their COVID-19 infections and deaths during the 15 April to 31 August 2020 period. These districts presented an active case burden of 559,566 and a cumulative 2,715,656 infections as of August 31. The epidemiological data of these districts was fit to a susceptible-asymptomatic-infected-recovered-dead (SAIRD) model and the underlying epidemic parameters for each of these districts during the course of 4 months was estimated. We validated these parameters against known epidemiological characteristic distributions and analyzed them to understand their changes in space-time during the pandemic. Findings. The center of the burden of the current-active infections which on May 15 was in the large metro districts with most international access shifted continuously and smoothly shifted towards districts which could be accessed by domestic airports and by trains. A linear trend-analysis showed a continuous improvement in most epidemic parameters consistently across the districts with four categories of accessibility from an international travel perspective - large metro, metro, urban and sub-urban districts. The reproduction numbers improved from 1.77±0.58 on May 15 to 1.07± 0.13 on August 31 in large metro districts (p-Value of trend 0.0001053); and from 1.58±0.39 on May 15 to 0.94±0.11 on August 31 in sub-urban districts (p-Value of trend 0.0067). The recovery rate per infected person per day improved from 0.0581±0.009 on May 15 to 0.091±0.010 on August 31 in large metro districts (p-Value of trend 0.26.10^{-12}); and from 0.059±0.011 on May 15 to 0.100±0.010 on August 31 in sub-urban districts (p-Value of trend 0.12.10{-16}). The death rate of symptomatic individuals which includes the case-fatality-rate as well as the time from symptoms to death, consistently decreased from 0.0025±0.0014 on May 15 to 0.0013±0.0003 on August 31 in large metro districts (p-Value of trend 0.0010); and from 0.0018±0.0008 on May 15 to 0.0014±0.0003 on August 31 in sub-urban districts (p-Value of trend 0.2789. Interpretation. As the daily infections continue to rise at a national level, it is important to notice a `local-flattening' in larger metro districts, and a shift of the pandemic-burden towards smaller sized districts in a clear hierarchical fashion of accessibility from an international travel perspective. The pandemic burden shifting towards remotely accessible regions, with possibly lesser health care facilities, is a call for attention to the re-organization of resources.

show abstract

Minimal and adaptive numerical strategy for critical resource planning in a pandemic

Cited by 10 publications

References 14 publications

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

Predicting and interpreting COVID-19 transmission rates from the ensemble of government policies

A steady trickle-down from metro districts and improving epidemic-parameters characterized the increasing COVID-19 cases in India

Contact Info

Product

Resources

About