A minimal and adaptive prediction strategy for critical resource planning in a pandemic

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

doi:10.1101/2020.04.08.20057414

Cited by 21 publications

(18 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, by May 18, many nations are close to the flattening of their epidemic curves. Besides the above results, Prakash et al [31] reported a linear growth of I(t) after early exponential growth.…”

Section: Introductionmentioning

confidence: 74%

Evolution of COVID-19 pandemic: Power-law growth and saturation

Chatterjee

Shayak

Ali

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, we analyze the real-time infection data of COVID-19 epidemic for 21 nations up to May 18, 2020. For most of these nations, the total number of infected individuals exhibits a succession of exponential growth and power-law growth before the flattening of the curve. In particular, we find a universal √ t growth before they reach saturation. India, Singapore, and Sri Lanka have reached up to linear growth (I(t) ∼ t), and they are yet to flatten their curves. Russia and Brazil are still in the power-law (t 2 ) growth regime. Thus, the polynomials of the I(t) curves provide valuable information on the stage of the epidemic evolution. Besides these detailed analyses, we compare the predictions of an extended SEIR model and a delay differential equation-based model with the reported infection data and observed good agreement among them, including the √ t behaviour.

show abstract

Section: Introductionmentioning

confidence: 74%

Evolution of COVID-19 pandemic: Power-law growth and saturation

Chatterjee

Shayak

Ali

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…We thus provide a mechanistic model for short term prediction of COVID-19 epidemic data. Compared to empirical measures 14,16,18 this method recreates data using parameters that are intuitive and directly related to operation on the field. While similar to other mechanistic models 3,4 , it differs in its design focus on separating observable and non observable compartments.…”

Section: Discussionmentioning

confidence: 99%

Using epidemic simulators for monitoring an ongoing epidemic

Raghavan¹,

Sridharan²,

Rangayyan³

2020

Sci Rep

View full text Add to dashboard Cite

Prediction of infection trends, estimating the efficacy of contact tracing, testing or impact of influx of infected are of vital importance for administration during an ongoing epidemic. Most effective methods currently are empirical in nature and their relation to parameters of interest to administrators are not evident. We thus propose a modified SEIRD model that is capable of modeling effect of interventions and inward migrations on the progress of an epidemic. The tunable parameters of this model bear relevance to monitoring of an epidemic. This model was used to show that some of the commonly seen features of cumulative infections in real data can be explained by piecewise constant changes in interventions and population influx. We also show that the data of cumulative infections from twelve Indian states between mid March and mid April 2020 can be generated from the model by applying interventions according to a set of heuristic rules. Prediction for the next ten days based on this model, reproduced real data very well. In addition, our model also reproduced the time series of recoveries and deaths. Our work constitutes an important first step towards an effective dashboard for the monitoring of epidemic by the administration, especially in an Indian context.

show abstract

“…Predominantly, epidemiological models were used to predict the spread of the virus on a national and regional level. The models predicted the number of COVID-19 infections and hospital admissions, subsequently translated into bed requirements and deaths [28,22,10,31,24]. Additionally, epidemiological models were extended by incorporating lock-down measures and other non-pharmaceutical interventions to inform public policy [19].…”

Section: Pandemic Resource Prediction Modellingmentioning

confidence: 99%

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Melman¹,

Parlikad²,

Cameron³

2020

Preprint

View full text Add to dashboard Cite

COVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke's hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

show abstract

A minimal and adaptive prediction strategy for critical resource planning in a pandemic

Cited by 21 publications

References 15 publications

Evolution of COVID-19 pandemic: Power-law growth and saturation

Evolution of COVID-19 pandemic: Power-law growth and saturation

Using epidemic simulators for monitoring an ongoing epidemic

Balancing Scarce Hospital Resources During a Pandemic: A Case-Study of COVID-19 at Addenbrooke’s Hospital (UK)

Contact Info

Product

Resources

About