Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making.

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture-recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

Direct and Indirect Effects of the Coronavirus Disease 2019 Pandemic on Private Healthcare Utilization in South Africa, March 2020-September 2021.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused severe disruptions to healthcare in many areas of the world, but data remain scarce for sub-Saharan Africa. METHODS: We evaluated trends in hospital admissions and outpatient emergency department (ED) and general practitioner (GP) visits to South Africa's largest private healthcare system during 2016-2021. We fit time series models to historical data and, for March 2020-September 2021, quantified changes in encounters relative to baseline. RESULTS: The nationwide lockdown on 27 March 2020 led to sharp reductions in care-seeking behavior that persisted for 18 months after initial declines. For example, total admissions dropped 59.6% (95% confidence interval [CI], 52.4-66.8) during home confinement and were 33.2% (95% CI, 29-37.4) below baseline in September 2021. We identified 3 waves of all-cause respiratory encounters consistent with COVID-19 activity. Intestinal infections and non-COVID-19 respiratory illnesses experienced the most pronounced declines, with some diagnoses reduced 80%, even as nonpharmaceutical interventions (NPIs) relaxed. Non-respiratory hospitalizations, including injuries and acute illnesses, were 20%-60% below baseline throughout the pandemic and exhibited strong temporal associations with NPIs and mobility. ED attendances exhibited trends similar to those for hospitalizations, while GP visits were less impacted and have returned to pre-pandemic levels. CONCLUSIONS: We found substantially reduced use of health services during the pandemic for a range of conditions unrelated to COVID-19. Persistent declines in hospitalizations and ED visits indicate that high-risk patients are still delaying seeking care, which could lead to morbidity or mortality increases in the future.

Transmission of Nipah Virus - 14 Years of Investigations in Bangladesh.

BACKGROUND: Nipah virus is a highly virulent zoonotic pathogen that can be transmitted between humans. Understanding the dynamics of person-to-person transmission is key to designing effective interventions. METHODS: We used data from all Nipah virus cases identified during outbreak investigations in Bangladesh from April 2001 through April 2014 to investigate case-patient characteristics associated with onward transmission and factors associated with the risk of infection among patient contacts. RESULTS: Of 248 Nipah virus cases identified, 82 were caused by person-to-person transmission, corresponding to a reproduction number (i.e., the average number of secondary cases per case patient) of 0.33 (95% confidence interval [CI], 0.19 to 0.59). The predicted reproduction number increased with the case patient's age and was highest among patients 45 years of age or older who had difficulty breathing (1.1; 95% CI, 0.4 to 3.2). Case patients who did not have difficulty breathing infected 0.05 times as many contacts (95% CI, 0.01 to 0.3) as other case patients did. Serologic testing of 1863 asymptomatic contacts revealed no infections. Spouses of case patients were more often infected (8 of 56 [14%]) than other close family members (7 of 547 [1.3%]) or other contacts (18 of 1996 [0.9%]). The risk of infection increased with increased duration of exposure of the contacts (adjusted odds ratio for exposure of >48 hours vs. ≤1 hour, 13; 95% CI, 2.6 to 62) and with exposure to body fluids (adjusted odds ratio, 4.3; 95% CI, 1.6 to 11). CONCLUSIONS: Increasing age and respiratory symptoms were indicators of infectivity of Nipah virus. Interventions to control person-to-person transmission should aim to reduce exposure to body fluids. (Funded by the National Institutes of Health and others.).

The Role of Remdesivir in South Africa: Preventing COVID-19 Deaths Through Increasing Intensive Care Unit Capacity.

Countries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of patients with COVID-19 requiring ICU care. Remdesivir can prevent deaths in countries such as South Africa by decreasing the number of days people spend in ICU, therefore freeing up ICU bed capacity.

Changing Contact Patterns Over Disease Progression: Nipah Virus as a Case Study.

Contact patterns play a key role in disease transmission, and variation in contacts during the course of illness can influence transmission, particularly when accompanied by changes in host infectiousness. We used surveys among 1642 contacts of 94 Nipah virus case patients in Bangladesh to determine how contact patterns (physical and with bodily fluids) changed as disease progressed in severity. The number of contacts increased with severity and, for case patients who died, peaked on the day of death. Given transmission has only been observed among fatal cases of Nipah virus infection, our findings suggest that changes in contact patterns during illness contribute to risk of infection.

Key questions for modelling COVID-19 exit strategies.

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Evidence for Mother-to-Child Transmission of Zika Virus Through Breast Milk.

Zikavirus (ZIKV) is an emerging viral pathogen that continues to spread throughout different regions of the world. Herein we report a case that provides further evidence that ZIKV transmission can occur through breastfeeding by providing a detailed clinical, genomic, and virological case-based description.

Detecting differential transmissibilities that affect the size of self-limited outbreaks.

Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying [0 < R eff < 1) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in R eff, suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.

How to make epidemiological training infectious.

Modern infectious disease epidemiology builds on two independently developed fields: classical epidemiology and dynamical epidemiology. Over the past decade, integration of the two fields has increased in research practice, but training options within the fields remain distinct with few opportunities for integration in the classroom. The annual Clinic on the Meaningful Modeling of Epidemiological Data (MMED) at the African Institute for Mathematical Sciences has begun to address this gap. MMED offers participants exposure to a broad range of concepts and techniques from both epidemiological traditions. During MMED 2010 we developed a pedagogical approach that bridges the traditional distinction between classical and dynamical epidemiology and can be used at multiple educational levels, from high school to graduate level courses. The approach is hands-on, consisting of a real-time simulation of a stochastic outbreak in course participants, including realistic data reporting, followed by a variety of mathematical and statistical analyses, stemming from both epidemiological traditions. During the exercise, dynamical epidemiologists developed empirical skills such as study design and learned concepts of bias while classical epidemiologists were trained in systems thinking and began to understand epidemics as dynamic nonlinear processes. We believe this type of integrated educational tool will prove extremely valuable in the training of future infectious disease epidemiologists. We also believe that such interdisciplinary training will be critical for local capacity building in analytical epidemiology as Africa continues to produce new cohorts of well-trained mathematicians, statisticians, and scientists. And because the lessons draw on skills and concepts from many fields in biology--from pathogen biology, evolutionary dynamics of host--pathogen interactions, and the ecology of infectious disease to bioinformatics, computational biology, and statistics--this exercise can be incorporated into a broad array of life sciences courses.

The application of one health approaches to henipavirus research.

Henipaviruses cause fatal infection in humans and domestic animals. Transmission from fruit bats, the wildlife reservoirs of henipaviruses, is putatively driven (at least in part) by anthropogenic changes that alter host ecology. Human and domestic animal fatalities occur regularly in Asia and Australia, but recent findings suggest henipaviruses are present in bats across the Old World tropics. We review the application of the One Health approach to henipavirus research in three locations: Australia, Malaysia and Bangladesh. We propose that by recognising and addressing the complex interaction among human, domestic animal and wildlife systems, research within the One Health paradigm will be more successful in mitigating future human and domestic animal deaths from henipavirus infection than alternative single-discipline approaches.

A comparison of bats and rodents as reservoirs of zoonotic viruses: are bats special?

Bats are the natural reservoirs of a number of high-impact viral zoonoses. We present a quantitative analysis to address the hypothesis that bats are unique in their propensity to host zoonotic viruses based on a comparison with rodents, another important host order. We found that bats indeed host more zoonotic viruses per species than rodents, and we identified life-history and ecological factors that promote zoonotic viral richness. More zoonotic viruses are hosted by species whose distributions overlap with a greater number of other species in the same taxonomic order (sympatry). Specifically in bats, there was evidence for increased zoonotic viral richness in species with smaller litters (one young), greater longevity and more litters per year. Furthermore, our results point to a new hypothesis to explain in part why bats host more zoonotic viruses per species: the stronger effect of sympatry in bats and more viruses shared between bat species suggests that interspecific transmission is more prevalent among bats than among rodents. Although bats host more zoonotic viruses per species, the total number of zoonotic viruses identified in bats (61) was lower than in rodents (68), a result of there being approximately twice the number of rodent species as bat species. Therefore, rodents should still be a serious concern as reservoirs of emerging viruses. These findings shed light on disease emergence and perpetuation mechanisms and may help lead to a predictive framework for identifying future emerging infectious virus reservoirs.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves.

OBJECTIVES: The aim of this study was to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources, and using data from public and private sector service providers. METHODS: R was estimated from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalisations, and hospital-associated deaths, using a method that models daily incidence as a weighted sum of past incidence, as implemented in the R package EpiEstim. R was also estimated separately using public and private sector data. RESULTS: Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves, respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves, but higher during the fourth wave for case-based estimates. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. CONCLUSION: Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. The high R estimates for Omicron relative to earlier waves are interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.

Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19.

BACKGROUND: Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination. METHODS: We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices. RESULTS: Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation. CONCLUSION: The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.

The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa.

In March 2020 the South African COVID-19 Modelling Consortium was formed to support government planning for COVID-19 cases and related healthcare. Models were developed jointly by local disease modelling groups to estimate cases, resource needs and deaths due to COVID-19. The National COVID-19 Epi Model (NCEM) while initially developed as a deterministic compartmental model of SARS-Cov-2 transmission in the nine provinces of South Africa, was adapted several times over the course of the first wave of infection in response to emerging local data and changing needs of government. By the end of the first wave, the NCEM had developed into a stochastic, spatially-explicit compartmental transmission model to estimate the total and reported incidence of COVID-19 across the 52 districts of South Africa. The model adopted a generalised Susceptible-Exposed-Infectious-Removed structure that accounted for the clinical profile of SARS-COV-2 (asymptomatic, mild, severe and critical cases) and avenues of treatment access (outpatient, and hospitalisation in non-ICU and ICU wards). Between end-March and early September 2020, the model was updated 11 times with four key releases to generate new sets of projections and scenario analyses to be shared with planners in the national and provincial Departments of Health, the National Treasury and other partners. Updates to model structure included finer spatial granularity, limited access to treatment, and the inclusion of behavioural heterogeneity in relation to the adoption of Public Health and Social Measures. These updates were made in response to local data and knowledge and the changing needs of the planners. The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africa's population and health system characteristics that played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.

Human Exposure to Bats, Rodents and Monkeys in Bangladesh.

Bats, rodents and monkeys are reservoirs for emerging zoonotic infections. We sought to describe the frequency of human exposure to these animals and the seasonal and geographic variation of these exposures in Bangladesh. During 2013-2016, we conducted a cross-sectional survey in a nationally representative sample of 10,002 households from 1001 randomly selected communities. We interviewed household members about exposures to bats, rodents and monkeys, including a key human-bat interface-raw date palm sap consumption. Respondents reported observing rodents (90%), bats (52%) and monkeys (2%) in or around their households, although fewer reported direct contact. The presence of monkeys around the household was reported more often in Sylhet division (7%) compared to other divisions. Households in Khulna (17%) and Rajshahi (13%) were more likely to report drinking date palm sap than in other divisions (1.5-5.6%). Date palm sap was mostly consumed during winter with higher frequencies in January (16%) and February (12%) than in other months (0-5.6%). There was a decreasing trend in drinking sap over the three years. Overall, we observed substantial geographic and seasonal patterns in human exposure to animals that could be sources of zoonotic disease. These findings could facilitate targeting emerging zoonoses surveillance, research and prevention efforts to areas and seasons with the highest levels of exposure.

The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making.

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics.

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.

Characterising differences in red blood cell usage patterns between healthcare sectors in South Africa: 2014-2019.

BACKGROUND: South Africa aims to transition from a two-tiered healthcare system (public and private) to universal health coverage. Data on red blood cell (RBC) product usage reveal disparities between the sectors. Blood transfusion services further need to understand differing disease profiles and transfusion prescribing practices between the sectors to ensure blood security should the transition to a two-tiered health system come to fruition. MATERIALS AND METHODS: Operational data for public and private healthcare RBC requests between 1 January 2014 and 31 March 2019, obtained from the South African National Blood Service (SANBS), were retrospectively analysed. Sector-specific demographic and utilisation trends were compared for the dominant clinical disciplines. Pre-transfusion haemoglobin (Hb) patterns were also delineated for 2018. RESULTS: Between 2014 and 2019, 2,356,411 public and private sector RBC transfusion events resulted in the issue of 4,020,094 RBC units (1,553,159 transfusion events and 2,495,054 units within the public sector versus 803,282 transfusion events and 1,525,040 units in private). The dominant clinical disciplines within the public sector were Medical (32.9%), Gynaecology/Obstetrics (27.3%), General Surgery (13.6%), and Paediatrics (including Paediatric Surgery) (6.5%), compared to Intensive Care Units (33.2%), Medical (28.3%), General Surgery (10.4%), and Haematology/Oncology (8.3%) in the private sector. Median pre-transfusion Hb values for 2018 were lower in the public than in the private sector: 6.9 g/dL public sector versus 8 g/dL private sector. DISCUSSION: Clinical drivers of RBC usage within the public and private healthcare sectors in South Africa differ significantly. Disparate pre-transfusion Hb between the sectors are likely due to differing disease profiles and severity, as well as differences in practice in prescribing transfusions. Implementation of a nationally co-ordinated Patient Blood Management programme may help to address these disparities and help ensure a sustainable blood transfusion system.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves.

Objectives: We aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers. Methods: We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data. Results: Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave. Discussion: Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa.

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.