Using real-time data to guide decision-making during an influenza pandemic: A modelling analysis.

Influenza pandemics typically occur in multiple waves of infection, often associated with initial emergence of a novel virus, followed (in temperate regions) by a resurgence accompanying the onset of the annual influenza season. Here, we examined whether data collected from an initial pandemic wave could be informative, for the need to implement non-pharmaceutical measures in any resurgent wave. Drawing from the 2009 H1N1 pandemic in 10 states in the USA, we calibrated simple mathematical models of influenza transmission dynamics to data for laboratory confirmed hospitalisations during the initial 'spring' wave. We then projected pandemic outcomes (cumulative hospitalisations) during the fall wave, and compared these projections with data. Model results showed reasonable agreement for all states that reported a substantial number of cases in the spring wave. Using this model we propose a probabilistic decision framework that can be used to determine the need for preemptive measures such as postponing school openings, in advance of a fall wave. This work illustrates how model-based evidence synthesis, in real-time during an early pandemic wave, could be used to inform timely decisions for pandemic response.

Exploring the Impact of the COVID-19 Pandemic on Tuberculosis Care and Prevention.

The COVID-19 pandemic has set back the global tuberculosis (TB) response by several years. In 2020, access to TB prevention and care declined sharply, with TB notifications dropping by 18% compared to 2019. Declines were more pronounced in children, with a 24% drop in 0-14 year-olds and a 28% drop in 0-4 year-olds. As a result, in 2020 the number of deaths due to TB increased to 1.5 million across all ages, reversing a decade-long declining trend. Progress toward the UN High Level Meeting targets for 2022 is at risk, including the targets related to children for TB and drug-resistant TB treatments, and TB preventive therapy. Nonetheless, ending TB by 2030 as envisaged in the Sustainable Development Goals (SDGs) is still possible, but requires increased investments in accelerated case detection, subclinical TB, preventive therapy and an effective vaccine. Investing in TB could prepare the world better for fighting a future airborne pandemic.

Adaptive strategies for the deployment of rapid diagnostic tests for COVID-19: a modelling study

Background: Lateral flow assays (LFAs) for the rapid detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) provide an affordable, rapid and decentralised means for diagnosing coronavirus disease 2019 (COVID-19). Concentrating on urban areas in low- and middle-income countries, we examined whether ‘dynamic' screening algorithms, that adjust the use of confirmatory polymerase chain reaction (PCR) testing based on epidemiological conditions, could reduce cost without substantially reducing the impact of testing. Methods: : Concentrating on a hypothetical ‘second wave' of COVID-19 in India, we modelled the potential impact of testing 0.5% of the population per day at random with LFA, regardless of symptom status. We considered dynamic testing strategies where LFA positive cases are only confirmed with PCR when LFA positivity rates are below a given threshold (relative to the peak positive rate at the height of the epidemic wave), compared to confirming either all positive LFA results or confirming no results. Benefit was estimated based on cumulative incidence of infection, and resource requirements, based on the cumulative number of PCR tests used and the cumulative number of unnecessary isolations. Results: : A dynamic strategy of discontinuing PCR confirmation when LFA positivity exceeded 50% of the peak positivity rate in an unmitigated epidemic would achieve comparable impact to one employing PCR confirmation throughout (9.2% of cumulative cases averted vs 9.8%), while requiring 35% as many PCR tests. However, the dynamic testing strategy would increase the number of false-positive test results substantially, from 0.07% of the population to 1.1%. Conclusions: : Dynamic diagnostic strategies that adjust to epidemic conditions could help maximise the impact of testing at a given cost. Generally, dynamic strategies reduce the number of confirmatory PCR tests needed, but increase the number of unnecessary isolations. Optimal strategies will depend on whether greater priority is placed on limiting confirmatory testing or false-positive diagnoses.

Symptom-based vs asymptomatic testing for controlling SARS-CoV-2 transmission in low- and middle-income countries: A modelling analysis.

BACKGROUND: Diagnostic testing plays a critical role in the global COVID-19 response. Polymerase chain reaction (PCR) tests are highly accurate, but in resource-limited settings, limited capacity has led to testing delays; whereas lateral flow assays (LFAs) offer opportunities for rapid and affordable testing. We examined the potential epidemiological impact of different strategies for LFA deployment. METHODS: We developed a deterministic compartmental model of SARS-CoV-2 transmission, parameterised to resemble a large Indian city. We assumed that PCR would be used to test symptomatic individuals presenting to outpatient settings for care. We examined how the second epidemic wave in India could have been mitigated by LFA deployment in its early stages by comparing two strategies: (i) community-based screening, using LFAs to test a proportion of the population, irrespective of symptoms (in addition to symptom-driven PCR), and (ii) symptom-driven outpatient testing, using LFAs to replace PCR. RESULTS: Model projections suggest that a stock of 25 million LFAs, used over a 600-day period in a city of 20 million people, would reduce the cumulative symptomatic incidence of COVID-19 by 0.44% if used for community-based screening, and by 13% if used to test symptomatic outpatients, relative to a no-LFA, PCR-only scenario. Sensitivity analysis suggests that outpatient testing would be more efficient in reducing transmission than community-based screening, when at least 5% of people with symptomatic COVID-19 seek care, and at least 10% of SARS-CoV-2 infections develop symptoms. Under both strategies, however, 2% of the population would be unnecessarily isolated. INTERPRETATION: In this emblematic setting, LFAs would reduce transmission most efficiently when used to test symptomatic individuals in outpatient settings. To avoid large numbers of unnecessary isolations, mass testing with LFAs should be considered as a screening tool, with follow-up confirmation. Future work should address strategies for targeted community-based LFA testing, such as contact tracing.

'Imperfect but useful': pandemic response in the Global South can benefit from greater use of mathematical modelling.

Mathematical modelling has been a helpful resource for planning public health responses to COVID-19. However, there is a need to improve the accessibility of models built within country contexts in the Global South. Immediately following the overwhelming 'second wave' of COVID-19 in India, we developed a user-friendly, web-based modelling simulator in partnership with the public health experts and health administrators for subnational planning. The purpose was to help policy-makers and programme officials at the state and district levels, to construct model-based scenarios for a possible third wave. Here, we describe our experiences of developing and deploying the simulator and propose the following recommendations for future such initiatives: early preparation will be the key for pandemic management planning, including establishment of networks with potential simulator users. Ideally, this preparedness should be conducted during 'peace time', and coordinated by agencies such as WHO. Second, flexible modelling frameworks will be needed, to respond rapidly to future emergencies as the precise nature of any pandemic is impossible to predict. Modelling resources will, therefore, need to be rapidly adaptable to respond as soon as a novel pathogen emerges. Third, limitations of modelling must be communicated clearly and consistently to end users. Finally, systematic mechanisms are required for monitoring the use of models in decision making, which will help in providing modelling support to those local authorities who may benefit most from it. Overall, these lessons from India can be relevant for other countries in the South-Asian-Region, to incorporate modelling resources into their pandemic preparedness planning.

A global system for the next generation of vaccines.

COVID-19 has shown that hurdles can be overcome.

Economic impact of tuberculosis mortality in 120 countries and the cost of not achieving the Sustainable Development Goals tuberculosis targets: a full-income analysis.

BACKGROUND: The tuberculosis targets for the UN Sustainable Development Goals (SDGs) call for a 90% reduction in tuberculosis deaths by 2030, compared with 2015, but meeting this target now seems highly improbable. To assess the economic impact of not meeting the target until 2045, we estimated full-income losses in 120 countries, including those due to excess deaths resulting from COVID-19-related disruptions to tuberculosis services, for the period 2020-50. METHODS: Annual mortality risk changes at each age in each year from 2020 to 2050 were estimated for 120 countries. This risk change was then converted to full-income risk by calculating a population-level mortality risk change and multiplying it by the value of a statistical life-year in each country and year. As a comparator, we assumed that current rates of tuberculosis continue to decline through the period of analysis. We calculated the full-income losses, and mean life expectancy losses per person, at birth and at age 35 years, under scenarios in which the SDG targets are met in 2030 and in 2045. We defined the cost of inaction as the difference in full-income losses and tuberculosis mortality between these two scenarios. FINDINGS: From 2020 to 2050, based on the current annual decrease in tuberculosis deaths of 2%, 31·8 million tuberculosis deaths (95% uncertainty interval 25·2 million-39·5 million) are estimated to occur, corresponding to an economic loss of US$17·5 trillion (14·9 trillion-20·4 trillion). If the SDG tuberculosis mortality target is met in 2030, 23·8 million tuberculosis deaths (18·9 million-29·5 million) and $13·1 trillion (11·2 trillion-15·3 trillion) in economic losses can be avoided. If the target is met in 2045, 18·1 million tuberculosis deaths (14·3 million-22·4 million) and $10·2 trillion (8·7 trillion-11·8 trillion) can be avoided. The cost of inaction of not meeting the SDG tuberculosis mortality target until 2045 (vs 2030) is, therefore, 5·7 million tuberculosis deaths (5·1 million-8·1 million) and $3·0 trillion (2·5 trillion-3·5 trillion) in economic losses. COVID-19-related disruptions add $290·3 billion (260·2 billion-570·1 billion) to this cost. INTERPRETATION: Failure to achieve the SDG tuberculosis mortality target by 2030 will lead to profound economic and health losses. The effects of delay will be greatest in sub-Saharan Africa. Affected countries, donor nations, and the private sector should redouble efforts to finance tuberculosis programmes and research because the economic dividend of such strategies is likely to be substantial. FUNDING: None.

Antigen-based Rapid Diagnostic Testing or Alternatives for Diagnosis of Symptomatic COVID-19: A Simulation-based Net Benefit Analysis.

BACKGROUND: SARS-CoV-2 antigen-detection rapid diagnostic tests can diagnose COVID-19 rapidly and at low cost, but lower sensitivity compared with reverse-transcriptase polymerase chain reaction (PCR) has limited clinical adoption. METHODS: We compared antigen testing, PCR testing, and clinical judgment alone for diagnosing symptomatic COVID-19 in an outpatient setting (10% COVID-19 prevalence among the patients tested, 3-day PCR turnaround) and a hospital setting (40% prevalence, 24-hour PCR turnaround). We simulated transmission from cases and contacts, and relationships between time, viral burden, transmission, and case detection. We compared diagnostic approaches using a measure of net benefit that incorporated both clinical and public health benefits and harms of the intervention. RESULTS: In the outpatient setting, we estimated that using antigen testing instead of PCR to test 200 individuals could be equivalent to preventing all symptomatic transmission from one person with COVID-19 (one "transmission-equivalent"). In a hospital, net benefit analysis favored PCR and testing 25 patients with PCR instead of antigen testing achieved one transmission-equivalent of benefit. In both settings, antigen testing was preferable to PCR if PCR turnaround time exceeded 2 days. Both tests provided greater net benefit than management based on clinical judgment alone unless intervention carried minimal harm and was provided equally regardless of diagnostic approach. CONCLUSIONS: For diagnosis of symptomatic COVID-19, we estimated that the speed of diagnosis with antigen testing is likely to outweigh its lower accuracy compared with PCR, wherever PCR turnaround time is 2 days or longer. This advantage may be even greater if antigen tests are also less expensive.

Plausibility of a third wave of COVID-19 in India: A mathematical modelling based analysis.

BACKGROUND & OBJECTIVES: In the context of India's ongoing resurgence of COVID-19 (second wave since mid-February 2021, following the subsiding of the first wave in September 2020), there has been increasing speculation on the possibility of a future third wave of infection, posing a burden on the healthcare system. Using simple mathematical models of the transmission dynamics of SARS-CoV-2, this study examined the conditions under which a serious third wave could occur. METHODS: Using a deterministic, compartmental model of SARS-CoV-2 transmission, four potential mechanisms for a third wave were examined: (i) waning immunity restores previously exposed individuals to a susceptible state, (ii) emergence of a new viral variant that is capable of escaping immunity to previously circulating strains, (iii) emergence of a new viral variant that is more transmissible than the previously circulating strains, and (iv) release of current lockdowns affording fresh opportunities for transmission. RESULTS: Immune-mediated mechanisms (waning immunity, or viral evolution for immune escape) are unlikely to drive a severe third wave if acting on their own, unless such mechanisms lead to a complete loss of protection among those previously exposed. Likewise, a new, more transmissible variant would have to exceed a high threshold (R0>4.5) to cause a third wave on its own. However, plausible mechanisms for a third wave include: (i) a new variant that is more transmissible and at the same time capable of escaping prior immunity, and (ii) lockdowns that are highly effective in limiting transmission and subsequently released. In both cases, any third wave seems unlikely to be as severe as the second wave. Rapid scale-up of vaccination efforts could play an important role in mitigating these and future waves of the disease. INTERPRETATION & CONCLUSIONS: This study demonstrates plausible mechanisms by which a substantial third wave could occur, while also illustrating that it is unlikely for any such resurgence to be as large as the second wave. Model projections are, however, subject to several uncertainties, and it remains important to scale up vaccination coverage to mitigate against any eventuality. Preparedness planning for any potential future wave will benefit by drawing upon the projected numbers based on the present modelling exercise.

India's pragmatic vaccination strategy against COVID-19: a mathematical modelling-based analysis.

OBJECTIVES: To investigate the impact of targeted vaccination strategies on morbidity and mortality due to COVID-19, as well as on the incidence of SARS-CoV-2, in India. DESIGN: Mathematical modelling. SETTINGS: Indian epidemic of COVID-19 and vulnerable population. DATA SOURCES: Country-specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain. MODEL: An age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed. INTERVENTIONS: Comparison of different vaccine strategies by targeting priority groups such as keyworkers including healthcare professionals, individuals with comorbidities (24-60 years old) and all above 60. MAIN OUTCOME MEASURES: Incidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented. RESULTS: The priority groups together account for about 18% of India's population. An infection-preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (UI) 16.7-25.4) and cumulative mortality by 29.7% (95% CrI 25.8-33.8). A similar vaccine with ability to prevent symptoms (but not infection) will reduce peak incidence of symptomatic cases by 10.4% (95% CrI 8.4-13.0) and cumulative mortality by 32.9% (95% CrI 28.6-37.3). In the event of insufficient vaccine supply to cover all priority groups, model projections suggest that after keyworkers, vaccine strategy should prioritise all who are >60 and subsequently individuals with comorbidities. In settings with weakest transmission, such as sparsely populated rural areas, those with comorbidities should be prioritised after keyworkers. CONCLUSIONS: An appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogeneity. 'Smart vaccination', based on public health considerations, rather than mass vaccination, appears prudent.

Quantifying heterogeneity in SARS-CoV-2 transmission during the lockdown in India.

The novel SARS-CoV-2 virus, as it manifested in India in April 2020, showed marked heterogeneity in its transmission. Here, we used data collected from contact tracing during the lockdown in response to the first wave of COVID-19 in Punjab, a major state in India, to quantify this heterogeneity, and to examine implications for transmission dynamics. We found evidence of heterogeneity acting at multiple levels: in the number of potentially infectious contacts per index case, and in the per-contact risk of infection. Incorporating these findings in simple mathematical models of disease transmission reveals that these heterogeneities act in combination to strongly influence transmission dynamics. Standard approaches, such as representing heterogeneity through secondary case distributions, could be biased by neglecting these underlying interactions between heterogeneities. We discuss implications for policy, and for more efficient contact tracing in resource-constrained settings such as India. Our results highlight how contact tracing, an important public health measure, can also provide important insights into epidemic spread and control.

Quantifying the potential value of antigen-detection rapid diagnostic tests for COVID-19: a modelling analysis.

BACKGROUND: Testing plays a critical role in treatment and prevention responses to the COVID-19 pandemic. Compared to nucleic acid tests (NATs), antigen-detection rapid diagnostic tests (Ag-RDTs) can be more accessible, but typically have lower sensitivity and specificity. By quantifying these trade-offs, we aimed to inform decisions about when an Ag-RDT would offer greater public health value than reliance on NAT. METHODS: Following an expert consultation, we selected two use cases for analysis: rapid identification of people with COVID-19 amongst patients admitted with respiratory symptoms in a 'hospital' setting and early identification and isolation of people with mildly symptomatic COVID-19 in a 'community' setting. Using decision analysis, we evaluated the health system cost and health impact (deaths averted and infectious days isolated) of an Ag-RDT-led strategy, compared to a strategy based on NAT and clinical judgement. We adopted a broad range of values for 'contextual' parameters relevant to a range of settings, including the availability of NAT and the performance of clinical judgement. We performed a multivariate sensitivity analysis to all of these parameters. RESULTS: In a hospital setting, an Ag-RDT-led strategy would avert more deaths than a NAT-based strategy, and at lower cost per death averted, when the sensitivity of clinical judgement is less than 90%, and when NAT results are available in time to inform clinical decision-making for less than 85% of patients. The use of an Ag-RDT is robustly supported in community settings, where it would avert more transmission at lower cost than relying on NAT alone, under a wide range of assumptions. CONCLUSIONS: Despite their imperfect sensitivity and specificity, Ag-RDTs have the potential to be simultaneously more impactful, and have a lower cost per death and infectious person-days averted, than current approaches to COVID-19 diagnostic testing.

The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis.

BACKGROUND: Routine services for tuberculosis (TB) are being disrupted by stringent lockdowns against the novel SARS-CoV-2 virus. We sought to estimate the potential long-term epidemiological impact of such disruptions on TB burden in high-burden countries, and how this negative impact could be mitigated. METHODS: We adapted mathematical models of TB transmission in three high-burden countries (India, Kenya and Ukraine) to incorporate lockdown-associated disruptions in the TB care cascade. The anticipated level of disruption reflected consensus from a rapid expert consultation. We modelled the impact of these disruptions on TB incidence and mortality over the next five years, and also considered potential interventions to curtail this impact. FINDINGS: Even temporary disruptions can cause long-term increases in TB incidence and mortality. If lockdown-related disruptions cause a temporary 50% reduction in TB transmission, we estimated that a 3-month suspension of TB services, followed by 10 months to restore to normal, would cause, over the next 5 years, an additional 1⋅19 million TB cases (Crl 1⋅06-1⋅33) and 361,000 TB deaths (CrI 333-394 thousand) in India, 24,700 (16,100-44,700) TB cases and 12,500 deaths (8.8-17.8 thousand) in Kenya, and 4,350 (826-6,540) cases and 1,340 deaths (815-1,980) in Ukraine. The principal driver of these adverse impacts is the accumulation of undetected TB during a lockdown. We demonstrate how long term increases in TB burden could be averted in the short term through supplementary "catch-up" TB case detection and treatment, once restrictions are eased. INTERPRETATION: Lockdown-related disruptions can cause long-lasting increases in TB burden, but these negative effects can be mitigated with rapid restoration of TB services, and targeted interventions that are implemented as soon as restrictions are lifted. FUNDING: USAID and Stop TB Partnership.

Combining serology with case-detection, to allow the easing of restrictions against SARS-CoV-2: a modelling-based study in India.

India's lockdown and subsequent restrictions against SARS-CoV-2, if lifted without any other mitigations in place, could risk a second wave of infection. A test-and-isolate strategy, using PCR diagnostic tests, could help to minimise the impact of this second wave. Meanwhile, population-level serological surveillance can provide valuable insights into the level of immunity in the population. Using a mathematical model, consistent with an Indian megacity, we examined how seroprevalence data could guide a test-and-isolate strategy, for fully lifting restrictions. For example, if seroprevalence is 20% of the population, we show that a testing strategy needs to identify symptomatic cases within 5-8 days of symptom onset, in order to prevent a resurgent wave from overwhelming hospital capacity in the city. This estimate is robust to uncertainty in the effectiveness of the lockdown, as well as in immune protection against reinfection. To set these results in their economic context, we estimate that the weekly cost of such a PCR-based testing programme would be less than 2.1% of the weekly economic loss due to the lockdown. Our results illustrate how PCR-based testing and serological surveillance can be combined to design evidence-based policies, for lifting lockdowns in Indian cities and elsewhere.

Prudent public health intervention strategies to control the coronavirus disease 2019 transmission in India: A mathematical model-based approach.

Background & objectives: Coronavirus disease 2019 (COVID-19) has raised urgent questions about containment and mitigation, particularly in countries where the virus has not yet established human-to-human transmission. The objectives of this study were to find out if it was possible to prevent, or delay, the local outbreaks of COVID-19 through restrictions on travel from abroad and if the virus has already established in-country transmission, to what extent would its impact be mitigated through quarantine of symptomatic patients? Methods: These questions were addressed in the context of India, using simple mathematical models of infectious disease transmission. While there remained important uncertainties in the natural history of COVID-19, using hypothetical epidemic curves, some key findings were illustrated that appeared insensitive to model assumptions, as well as highlighting critical data gaps. Results: It was assumed that symptomatic quarantine would identify and quarantine 50 per cent of symptomatic individuals within three days of developing symptoms. In an optimistic scenario of the basic reproduction number (R0) being 1.5, and asymptomatic infections lacking any infectiousness, such measures would reduce the cumulative incidence by 62 per cent. In the pessimistic scenario of R0=4, and asymptomatic infections being half as infectious as symptomatic, this projected impact falls to two per cent. Interpretation & conclusions: Port-of-entry-based entry screening of travellers with suggestive clinical features and from COVID-19-affected countries, would achieve modest delays in the introduction of the virus into the community. Acting alone, however, such measures would be insufficient to delay the outbreak by weeks or longer. Once the virus establishes transmission within the community, quarantine of symptomatics may have a meaningful impact on disease burden. Model projections are subject to substantial uncertainty and can be further refined as more is understood about the natural history of infection of this novel virus. As a public health measure, health system and community preparedness would be critical to control any impending spread of COVID-19 in the country.