Forecasting Demand for Personal Protective Equipment for Ontario Acute Care Hospitals During the First Wave of the COVID-19 Pandemic

Maintaining an adequate supply of personal protective equipment (PPE) is a global challenge for health systems during the COVID-19 pandemic. Estimating PPE demand is critical for planning clinical activities and managing supplies. We used health system modelling to forecast PPE demand in Ontario’s acute care sector during the COVID-19 pandemic. In particular, we estimated PPE demand by integrating PPE requirements for patient contacts into an existing health system model that forecasts near-term (up to 60 days) COVID-19 cases and their care trajectory in Ontario’s acute care system. We modeled two PPE strategies to care for confirmed and suspected COVID-19 patients: a base case scenario considering provincial PPE use recommendations, and a hospital-level case study using a PPE policy of universal masking and conservation strategies. Our base case estimate of PPE required to care for confirmed or suspected COVID-19 patients in Ontario’s acute care hospitals over a 60-day period was substantial—over 4.5 million each of surgical masks, face shields, and gowns would be required. Our hospital-level case study demonstrated that reuse of PPE reduces demand and offsets the effects of healthcare worker point of care assessments for N95 masks. Our work shows that health system modelling can estimate demand for PPE across pandemic trajectories and PPE use policies. The PPE volume required to safely care for COVID-19 patients is substantial and different PPE policies have marked effects on demand, which needs to be taken into account in procurement decisions to ensure adequate supply. © 2022, Springer Nature Switzerland AG.

Covid-19: Adaptation of a model to predict healthcare resource needs in valle del Cauca, Colombia

Background: Valle del Cauca is the region with the fourth-highest number of COVID-19 cases in Colombia (>50,000 on September 7, 2020). Due to the lack of anti-COVID-19 therapies, decision-makers require timely and accurate data to estimate the incidence of disease and the availability of hospital resources to contain the pandemic. Methods: We adapted an existing model to the local context to forecast COVID-19 incidence and hospital resource use assuming different scenarios: (1) the implementation of quarantine from September 1st to October 15th (average daily growth rate of 2%);(2-3) partial restrictions (at 4% and 8% growth rates);and (4) no restrictions, assuming a 10% growth rate. Previous scenarios with predictions from June to August were also presented. We estimated the number of new cases, diagnostic tests required, and the number of available hospital and intensive care unit (ICU) beds (with and without ventilators) for each scenario. Results: We estimated 67,700 cases by October 15th when assuming the implementation of a quarantine, 80,400 and 101,500 cases when assuming partial restrictions at 4% and 8% infection rates, respectively, and 208,500 with no restrictions. According to different scenarios, the estimated demand for reverse transcription-polymerase chain reaction tests ranged from 202,000 to 1,610,600 between September 1st and October 15th. The model predicted depletion of hospital and ICU beds by September 20th if all restrictions were to be lifted and the infection growth rate increased to 10%. Conclusion: Slowly lifting social distancing restrictions and reopening the economy is not expected to result in full resource depletion by October if the daily growth rate is maintained below 8%. Increasing the number of available beds provides a safeguard against slightly higher infection rates. Predictive models can be iteratively used to obtain nuanced predictions to aid decision-making.