Lessons from COVID-19 for rescalable data collection

Mathematical modelling of the 100-day target for vaccine availability after the detection of a novel pathogen: A case study in Indonesia

Globally, there has been a commitment to produce and distribute a vaccine within 100 days of the next pandemic. This 100-day target will place pressure on countries to make swift decisions on how to optimise vaccine delivery. We used data from the COVID-19 pandemic to inform mathematical modelling of future pandemics in Indonesia for a wide range of pandemic characteristics. We explored the benefits of vaccination programs with different start dates, rollout capacity, and age-specific prioritisation within a year of the detection of a novel pathogen. Early vaccine availability, public uptake of vaccines, and capacity for consistent vaccine delivery were the key factors influencing vaccine benefit. Monitoring age-specific severity will be essential for optimising vaccine benefit. Our study complements existing pathogen-specific pandemic preparedness plans and contributes a tool for the rapid assessment of future threats in Indonesia and similar middle-income countries.

Efficacy and safety of ivermectin for treatment of non-hospitalized COVID-19 patients: A systematic review and meta-analysis of 12 randomized controlled trials with 7,035 participants

INTRODUCTION

We systematically assessed benefits and harms of the use of ivermectin in non-hospitalized patients with early COVID-19.

METHODS

Five databases were searched until October 17, 2023, for randomized controlled trials (RCTs) in adult patients with COVID-19 treated with ivermectin against standard of care (SoC), placebo, or active drug. Primary outcomes were hospitalization, all-cause mortality, and adverse events (AEs). Secondary outcomes included mechanical ventilation (MV), clinical improvement, clinical worsening, viral clearance, and severe adverse events (SAEs). Random effects meta-analyses were performed, with quality of evidence (QoE) evaluated using GRADE methods. Pre-specified subgroup analyses (ivermectin dose, control type, risk of bias, follow-up, and country income) and trial sequential analysis (TSA) were performed.

RESULTS

Twelve RCTs (n = 7,035) were included. The controls were placebo in nine RCTs, SoC in two RCTs, and placebo or active drug in one RCT. Ivermectin did not reduce hospitalization (relative risk [RR], 0.81, 95% confidence interval [95% CI] 0.64-1.03; 8 RCTs, low QoE), all-cause mortality (RR 0.98, 95% CI 0.73-1.33; 9 RCTs, low QoE), or AEs (RR 0.89, 95% CI 0.75-1.07; 9 RCTs, very low QoE) vs. controls. Ivermectin did not reduce MV, clinical worsening, or SAEs and did not increase clinical improvement and viral clearance vs. controls (very low QoE for secondary outcomes). Subgroup analyses were mostly consistent with main analyses, and TSA-adjusted risk for hospitalization was similar to main analysis.

CONCLUSIONS

In non-hospitalized COVID-19 patients, ivermectin did not have effect on clinical, non-clinical or safety outcomes versus controls. Ivermectin should not be recommended as treatment in non-hospitalized COVID-19 patients.

Inference of epidemic dynamics in the COVID-19 era and beyond

The COVID-19 pandemic demonstrated the key role that epidemiology and modelling play in analysing infectious threats and supporting decision making in real-time. Motivated by the unprecedented volume and breadth of data generated during the pandemic, we review modern opportunities for analysis to address questions that emerge during a major modern epidemic. Following the broad chronology of insights required - from understanding initial dynamics to retrospective evaluation of interventions, we describe the theoretical foundations of each approach and the underlying intuition. Through a series of case studies, we illustrate real life applications, and discuss implications for future work.

Pandemic burden in low-income settings and impact of limited and delayed interventions: A granular modelling analysis of COVID-19 in Kabwe, Zambia

OBJECTIVES

Pandemic response in low-income countries (LICs) or settings often suffers from scarce epidemic surveillance and constrained mitigation capacity. The drivers of pandemic burden in such settings, and the impact of limited and delayed interventions remain poorly understood.

METHODS

We analysed COVID-19 seroprevalence and all-cause excess deaths data from the peri-urban district of Kabwe, Zambia between March 2020 and September 2021 with a novel mathematical model. Data encompassed three consecutive waves caused by the wild-type, Beta and Delta variants.

RESULTS

Across all three waves, we estimated a high cumulative attack rate, with 78% (95% credible interval [CrI] 71-85) of the population infected, and a high all-cause excess mortality, at 402 (95% CrI 277-473) deaths per 100,000 people. Ambitiously improving health care to a capacity similar to that in high-income settings could have averted up to 46% (95% CrI 41-53) of accrued excess deaths, if implemented from June 2020 onward. An early and accelerated vaccination rollout could have achieved the highest reductions in deaths. Had vaccination started as in some high-income settings in December 2020 and with the same daily capacity (doses per 100 population), up to 68% (95% CrI 64-71) of accrued excess deaths could have been averted. Slower rollouts would have still averted 62% (95% CrI 58-68), 54% (95% CrI 49-61) or 26% (95% CrI 20-38) of excess deaths if matching the average vaccination capacity of upper-middle-, lower-middle- or LICs, respectively.

CONCLUSIONS

Robust quantitative analyses of pandemic data are of pressing need to inform future global pandemic preparedness commitments.

Theoretical Epidemiology Needs Urgent Attention in China

The mathematical method to which theoretical epidemiology belongs is one of the three major methodologies in epidemiology. It is of great value in diagnosing infectious disease epidemic trends and evaluating the effectiveness of prevention and control measures. This paper aims to summarize the brief history of the development of theoretical epidemiology, common types of mathematical models, and key steps to develop a mathematical model. It also provides some thoughts and perspectives on the development and application of theoretical epidemiology in China.

Phylogeny and molecular evolution of the first local monkeypox virus cluster in Guangdong Province, China

The first local mpox outbreak in Guangdong Province, China occurred in June 2023. However, epidemiological data have failed to quickly identify the source and transmission of the outbreak. Here, phylogeny and molecular evolution of 10 monkeypox virus (MPXV) genome sequences from the Guangdong outbreak were characterized, revealing local silent transmissions that may have occurred in Guangdong whose mpox outbreaks suggested a molecular epidemiological correlation with Portugal and several regions of China during the same period. The lineage IIb C.1, which includes all 10 MPXV from Guangdong, shows consistent temporal continuity in both phylogenetic characteristics and unique molecular evolutionary mutation spectrum, reflected in the continuous increase of single nucleotide polymorphisms (SNPs) and shared mutations over time. Compared with the Japan MPXV, the Guangdong MPXV showed higher genomic nucleotide differences and separated 14 shared mutations from the B.1 lineage, comprising 6 non-synonymous mutations in genes linked to host regulation, virus infection, and virus life cycle. The unique mutation spectrum with temporal continuity in IIb C.1, related to apolipoprotein B mRNA-editing catalytic polypeptide-like 3, promotes rapid viral evolution and diversification. The findings contribute to understanding the ongoing mpox outbreak in China and offer insights for developing joint prevention and control strategies.

Lessons identified for a future pandemic

Pandemics are complex events requiring a coordinated, global response. The response to the pandemic exposed vulnerabilities in system preparedness. Lessons arising from the COVID-19 pandemic are characterized by four broad themes: (i) investment in public health and health infrastructure, (ii) countermeasures (medical and non-medical), (iii) risk communication and public health measures and (iv) investment in people and partnerships. Learning from the COVID-19 pandemic identifies an approach that focusses on capacities and capabilities that are pathogen agnostic, ensuring that we can respond to diverse emerging infectious disease threats will be essential. The lessons learned from previous and ongoing infectious disease outbreaks should be kept under constant review, in line with technological and scientific advances, to improve our ability to detect, mitigate and respond to new and emerging threats.