Understanding the Potential Impact of Different Drug Properties on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission and Disease Burden: A Modelling Analysis

Abstract

Background: The public health impact of the coronavirus disease 2019 (COVID-19) pandemic has motivated a rapid search for potential therapeutics, with some key successes. However, the potential impact of different treatments, and consequently research and procurement priorities, have not been clear.

Methods: Using a mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission, COVID-19 disease and clinical care, we explore the public-health impact of different potential therapeutics, under a range of scenarios varying healthcare capacity, epidemic trajectories; and drug efficacy in the absence of supportive care.

Results: The impact of drugs like dexamethasone (delivered to the most critically-ill in hospital and whose therapeutic benefit is expected to depend on the availability of supportive care such as oxygen and mechanical ventilation) is likely to be limited in settings where healthcare capacity is lowest or where uncontrolled epidemics result in hospitals being overwhelmed. As such, it may avert 22% of deaths in high-income countries but only 8% in low-income countries (assuming R = 1.35). Therapeutics for different patient populations (those not in hospital, early in the course of infection) and types of benefit (reducing disease severity or infectiousness, preventing hospitalization) could have much greater benefits, particularly in resource-poor settings facing large epidemics.

Conclusions: Advances in the treatment of COVID-19 to date have been focused on hospitalized-patients and predicated on an assumption of adequate access to supportive care. Therapeutics delivered earlier in the course of infection that reduce the need for healthcare or reduce infectiousness could have significant impact, and research into their efficacy and means of delivery should be a priority.

Keywords: COVID-19; SARS-CoV-2; epidemiology; modelling; therapeutics.

Figure 1.

Mathematical modeling approach used to evaluate potential COVID-19 treatment impact. A, Schematic representation of the natural history of SARS-CoV-2 infection and COVID-19 disease in the model. B, Description of the different disease states included in the model and the associated healthcare requirements. C, Decision-tree diagrams illustrating the conditional delivery of healthcare components according to disease severity and availability. There is excess mortality associated with not receiving the full set of required healthcare components. Abbreviations: COVID-19, coronavirus disease 2019; SARS-COV-2, severe acute respiratory syndrome coronavirus 2.

Figure 2.

Projected impact of dexamethasone on COVID-19 mortality under different scenarios of epidemic progression and healthcare availability. A, Daily general hospital bed demand under an epidemic scenario with a high reproduction number (R = 2, orange) or a low reproduction number (R = 1.35, green). Dashed lines indicate availability of different healthcare resources, and the right hand panel describes the proportion of patients that require oxygen and a general hospital bed who receive complete (bed and oxygen), incomplete (bed only) or no healthcare (neither). B, As in panel (A) but describing demand and healthcare received for severely and critically ill patients requiring an ICU bed, oxygen, and ARS. C, Schematic illustration of the impact assumed for dexamethasone on COVID-19 mortality in different patient populations (moderate, severe or critical illness), and according to the care received (complete, incomplete or none). D, Impact of dexamethasone on the COVID-19 infection fatality ratio under different assumptions for R (low, green or high, orange) and healthcare availability (unlimited, limited ARS, limited ARS and oxygen or limited ARS, oxygen and beds). In all panels, black points show the IFR without dexamethasone, and the boxplots show the modelled IFR using the assumed dexamethasone clinical benefit estimates described in panel (C). E, Percentage of maximum potential dexamethasone impact (defined as the reduction in IFR achieved by dexamethasone under a situation of unlimited healthcare) achieved in each of the different scenarios for healthcare availability. Orange and green bars refer to high and low R scenarios, respectively, with the shading indicating the extent of imposed healthcare constraints, colored as for panel (D). Abbreviations: ARS, advanced respiratory support; COVID-19, coronavirus disease 2019; ICU, intensive care unit; IFR, infection fatality ratio.

Figure 3.

Global impact of dexamethasone on COVID-19 mortality under different assumptions for future transmission and epidemic spread. A, Percentage of maximum potential dexamethasone impact (defined as the reduction in IFR achieved by dexamethasone under a situation of unlimited healthcare) achieved for each country under an epidemic scenario of extensive mitigation control (R = 1.35). B, Percentage of maximum dexamethasone impact achieved in each country. Each dot is the result for a single country, colored according to the World Bank strata that country belongs to, with the boxplot presenting summary statistics for the modelled countries in aggregate. (C) As in panel (A), under an assumption of an epidemic scenario characterized by uncontrolled spread (R = 2). (D) As in panel (B), under an assumption of an epidemic scenario characterized by uncontrolled spread (R = 2). Abbreviations: COVID-19, coronavirus disease 2019; IFR, infection fatality ratio.

Figure 4.

Impact of different therapeutic product effects on COVID-19 disease burden. A, For an epidemic with an R of 1.35, the proportion of COVID-19 deaths averted as a function of therapeutic efficacy and therapeutic coverage, for 6 different types of potential effects (Table 1). These include reducing COVID-19 disease mortality (Type 1); preventing deterioration and worsening of disease in hospitalized patients (Type 2); reducing duration of hospitalization (Type 3); preventing hospitalization due to COVID-19 (Type 4) and reducing duration of infectiousness, either among symptomatic (Types 5a) or all infected-persons (Type 5b). Inset boxes indicate the range of plausible values of coverage used to generate the estimates in panel (B). B, Disaggregation of therapeutic effect type impact by whether this is direct or indirect. Bars are colored according to the type of impact (direct reduction in mortality, indirect reduction in mortality due to reduced pressure on healthcare or indirect reduction in mortality due to reductions in community transmission), with error bars indicating the maximum and minimum proportion of deaths averted under the range of coverage and effectiveness values considered for each effect type (indicated by the boxes in panel (A) and Table 1). Abbreviations: COVID-19, coronavirus disease 2019; IFR, infection fatality ratio.