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Progress towards the 2030 End TB goals has seen severe setbacks due to disruptions arising from the COVID-19 pandemic. For governments and international partner organizations supporting the global TB response, there is a need to assess what level of effort is now needed to reach these goals. Using mathematical modelling, we addressed this question for the countries being supported by the United States Agency for International Development (USAID). We aggregated the 24 countries in the USAID portfolio into three geographical country groups: South Asia; sub-Saharan Africa; and Central Asian Republics/Europe (CAR/EU). From 2023 onwards we modelled a combination of interventions acting at different stages of the care cascade, including improved diagnostics; reducing the patient care seeking delay; and the rollout of a disease-preventing vaccine from 2025 onwards. We found that in all three country groups, meeting the End TB goals by 2030 will require a combination of interventions acting at stages of the TB care cascade. Specific priorities may depend on country settings, for example with public-private mix playing an important role in countries in South Asia and elsewhere. When a vaccine becomes available, its required coverage to meet the 2030 goals will vary by setting, depending on the amount of preventive therapy that has already been implemented. Monitoring the number-needed-to-test to identify 1 person with TB in community settings can provide a useful measure of progress towards the End TB goals.
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ObjectivesTo 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. DesignMathematical modelling. SettingsIndian epidemic of COVID-19 and vulnerable population. Data sourcesCountry specific and age-segregated pattern of social contact, case fatality rate and demographic data obtained from peer-reviewed literature and public domain. ModelAn age-structured dynamical model describing SARS-CoV-2 transmission in India incorporating uncertainty in natural history parameters was constructed. InterventionsComparison of different vaccine strategies by targeting priority groups such as key workers including health care professionals, individuals with comorbidities (24 - 60 year), and all above 60. Main outcome measuresIncidence reduction and averted deaths in different scenarios, assuming that the current restrictions are fully lifted as vaccination is implemented. ResultsThe priority groups together account for about 18% of Indias population. An infection preventing vaccine with 60% efficacy covering all these groups would reduce peak symptomatic incidence by 20.6% (95% uncertainty intervals (CrI) 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. ConclusionsAn appropriately targeted vaccination strategy would witness substantial mitigation of impact of COVID-19 in a country like India with wide heterogenity. Smart vaccination, based on public health considerations, rather than mass vaccination, appears prudent. O_TEXTBOXStrengths and limitation of this study O_LIThe model in this study is informed by age-dependent risk factors for SARS-CoV-2 infection among contacts, and is stratified by co-morbidities (diabetes and/or hypertension), and vaccination status. C_LIO_LIData on mortality and large-scale contact tracing from within India, and the recent national sero-survey results were used, which constituted a major strength of this investigation. C_LIO_LIDistinguishing between infection and symptomatic disease preventing vaccines, the model was simulated under a range of scenarios for the basic reproduction number (R0). C_LIO_LIShould they have been available, real life country-specific data on excess risks of deaths due to comorbidities would have added strength to the presented model. C_LIO_LIKey priority group-specific data on social mixing and potential associated transmission was not available, and remained as a limitation. C_LI C_TEXTBOX
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BackgroundSARS-CoV-2 antigen-detection rapid diagnostic tests (Ag-RDTs) can diagnose COVID-19 rapidly and at low cost, but their lower sensitivity than nucleic acid amplification testing (NAAT) has limited clinical adoption. MethodsWe compared Ag-RDT, NAAT, and clinical judgment alone for diagnosing symptomatic COVID-19. We considered an outpatient setting (10% COVID-19 prevalence among the patients tested, 3-day NAAT turnaround) and a hospital setting (40% prevalence, 24-hour NAAT 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 intervention. ResultsIn the outpatient setting, we estimated that using Ag-RDT instead of NAAT to test 200 individuals could have a net benefit equivalent to preventing all symptomatic transmission from one person with COVID-19 (one "transmission-equivalent"). In the hospital setting, net benefit analysis favored NAAT, and testing 25 patients with NAAT instead of Ag-RDT achieved one "transmission-equivalent" of incremental benefit. In both settings, Ag-RDT was preferred to NAAT if NAAT turnaround time exceeded two days. Both Ag-RDT and NAAT provided greater net benefit than management based on clinical judgment alone, unless intervention carried minimal harm and was provided equally regardless of diagnostic approach. ConclusionsFor diagnosis of symptomatic COVID-19, the speed of diagnosis with Ag-RDT is likely to outweigh its lower accuracy compared to NAAT wherever NAAT turnaround times are two days or longer. This advantage may be even greater if Ag-RDTs are also less expensive.
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BackgroundTesting 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. MethodsFollowing 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 cost and impact (deaths averted and infectious days isolated) of an Ag-RDT-led strategy, compared to a strategy based on NAT and clinical judgment. We performed a multivariate sensitivity analysis to identify key parameters. ResultsIn 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 85%, and when NAT results are available in time to inform clinical decision-making for less than 90% 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. ConclusionsDespite their imperfect sensitivity and specificity, Ag-RDTs have the potential to be simultaneously more impactful, and cost-effective, than current approaches to COVID-19 diagnostic testing.
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The novel SARS-CoV-2 virus shows marked heterogeneity in its transmission. Here, we used data collected from contact tracing during the lockdown 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.
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Indias lockdown 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 lifting a lockdown. 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.
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BackgroundRoutine 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. MethodsWe 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. ResultsEven temporary disruptions can cause long-term increases in TB incidence and mortality. We estimated that a 3-month lockdown, followed by 10 months to restore normal TB services, would cause, over the next 5 years, an additional 1.65 million TB cases (Crl 1.49- 1.85) and 438,000 TB deaths (CrI 403 - 483 thousand) in India, 41,400 (28,900-62,200) TB cases and 14,800 deaths (10.5 - 19.2 thousand) in Kenya, and 7,960 (6,250 - 9,880) cases and 2,050 deaths (1,610 - 2,360) in Ukraine. However, any such negative impacts could be averted through supplementary "catch-up" TB case detection and treatment, once restrictions are eased. InterpretationLockdown-related disruptions can cause long-lasting increases in TB burden, but these negative effects can be mitigated with targeted interventions implemented rapidly once lockdowns are lifted.
