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The severe shortfall in testing supplies during the initial phases of the COVID-19 pandemic and ensuing struggle to control disease spread have affirmed the need to plan rigorous optimized supply-constrained resource allocation strategies for the next inevitable novel disease epidemic. To address the challenge of optimizing limited resource usage in the face of complicated disease dynamics, we develop an integro partial differential equation disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals, and we analyze the influence of these ele- ments on controllability and optimal resource allocation between two testing strategies, ‘clinical’ targeting symptomatic individuals and ‘non-clinical’ targeting non-symptomatic individuals, for reducing total infection sizes. We apply our model to not only the original, delta, and omicron COVID-19 variants, but also to generic diseases which have different offsets between latent and incubation period distributions which allow for or prevent varying degrees of presymptomatic transmission or preinfectiousness symptom onset. We find that factors which reduce control- lability generally call for reduced levels of non-clinical testing, while the relationship between symptom onset, controllability, and optimal strategies is complicated. Although greater degrees of presymptomatic transmission reduce disease controllability, they may enhance or reduce the role of non-clinical testing in optimal strategies depending on other disease factors like overall transmissibility and latent period length. Our model allows a spectrum of diseases to be com- pared under the same lens such that the lessons learned from COVID-19 can be adapted to resource constraints in the next emerging epidemic and analyzed for optimal strategies under a consistent mathematical framework.

The severe shortfall in testing supplies during the initial phases of the COVID-19 pandemic and ensuing struggle to control disease spread have affirmed the need to plan rigorous optimized supply-constrained resource allocation strategies for the next inevitable novel disease epidemic. To address the challenge of optimizing limited resource usage in the face of complicated disease dynamics, we develop an integro partial differential equation disease model which incorporates realistic latent, incubation, and infectious period distributions along with limited testing supplies for identifying and quarantining infected individuals, and we analyze the influence of these ele- ments on controllability and optimal resource allocation between two testing strategies, ‘clinical’ targeting symptomatic individuals and ‘non-clinical’ targeting non-symptomatic individuals, for reducing total infection sizes. We apply our model to not only the original, delta, and omicron COVID-19 variants, but also to generic diseases which have different offsets between latent and incubation period distributions which allow for or prevent varying degrees of presymptomatic transmission or preinfectiousness symptom onset. We find that factors which reduce control- lability generally call for reduced levels of non-clinical testing, while the relationship between symptom onset, controllability, and optimal strategies is complicated. Although greater degrees of presymptomatic transmission reduce disease controllability, they may enhance or reduce the role of non-clinical testing in optimal strategies depending on other disease factors like overall transmissibility and latent period length. Our model allows a spectrum of diseases to be com- pared under the same lens such that the lessons learned from COVID-19 can be adapted to resource constraints in the next emerging epidemic and analyzed for optimal strategies under a consistent mathematical framework.

Insufficient testing capacity continues to be a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and an information parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the information parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and unfocused (information=0) non-clinical testing becomes optimal. We further highlight the dvantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.

Insufficient testing capacity continues to be a critical bottleneck in the worldwide fight against COVID-19. Optimizing the deployment of limited testing resources has therefore emerged as a keystone problem in pandemic response planning. Here, we use a modified SEIR model to optimize testing strategies under a constraint of limited testing capacity. We define pre-symptomatic, asymptomatic, and symptomatic infected classes, and assume that positively tested individuals are immediately moved into quarantine. We further define two types of testing. Clinical testing focuses only on the symptomatic class. Non-clinical testing detects pre- and asymptomatic individuals from the general population, and an information parameter governs the degree to which such testing can be focused on high infection risk individuals. We then solve for the optimal mix of clinical and non-clinical testing as a function of both testing capacity and the information parameter. We find that purely clinical testing is optimal at very low testing capacities, supporting early guidance to ration tests for the sickest patients. Additionally, we find that a mix of clinical and non-clinical testing becomes optimal as testing capacity increases. At high but empirically observed testing capacities, a mix of clinical testing and unfocused (information=0) non-clinical testing becomes optimal. We further highlight the dvantages of early implementation of testing programs, and of combining optimized testing with contact reduction interventions such as lockdowns, social distancing, and masking.