COVID-19: a perspective for lifting lockdown in Zimbabwe

Governments will not be able to minimise both deaths from coronavirus disease 2019 (COVID-19) and the economic impact of viral spread. Keeping mortality as low as possible will be the highest priority for individuals; hence governments must put in place measures to ameliorate the inevitable economic downturn. In our view, COVID-19 has developed into a pandemic, with small chains of transmission in many countries and large chains resulting in extensive spread in a few countries, such as Italy, Iran, South Korea, and Japan. 1 Most countries are likely to have spread of COVID-19, at least in the early stages, before any mitigation measures have an impact. What has happened in China shows that quarantine, social distancing, and isolation of infected populations can contain the epidemic. 1 This impact of the COVID-19 response in China is encouraging for the many countries where COVID-19 is beginning to spread. However, it is unclear whether other countries can implement the stringent measures China eventually adopted. Singapore and Hong Kong, both of which had severe acute respiratory syndrome (SARS) epidemics in 2002–03, provide hope and many lessons to other countries. In both places, COVID-19 has been managed well to date, despite early cases, by early government action and through social distancing measures taken by individuals. The course of an epidemic is defined by a series of key factors, some of which are poorly understood at present for COVID-19. The basic reproduction number (R0), which defines the mean number of secondary cases generated by one primary case when the population is largely susceptible to infection, determines the overall number of people who are likely to be infected, or more precisely the area under the epidemic curve. For an epidemic to take hold, the value of R0 must be greater than unity in value. A simple calculation gives the fraction likely to be infected without mitigation. This fraction is roughly 1–1/R0. With R0 values for COVID-19 in China around 2·5 in the early stages of the epidemic, 2 we calculate that approximately 60% of the population would become infected. This is a very worst-case scenario for a number of reasons. We are uncertain about transmission in children, some communities are remote and unlikely to be exposed, voluntary social distancing by individuals and communities will have an impact, and mitigation efforts, such as the measures put in place in China, greatly reduce transmission. As an epidemic progresses, the effective reproduction number (R) declines until it falls below unity in value when the epidemic peaks and then decays, either due to the exhaustion of people susceptible to infection or the impact of control measures. The speed of the initial spread of the epidemic, its doubling time, or the related serial interval (the mean time it takes for an infected person to pass on the infection to others), and the likely duration of the epidemic are determined by factors such as the length of time from infection to when a person is infectious to others and the mean duration of infectiousness. For the 2009 influenza A H1N1 pandemic, in most infected people these epidemiological quantities were short with a day or so to infectiousness and a few days of peak infectiousness to others. 3 By contrast, for COVID-19, the serial interval is estimated at 4·4–7·5 days, which is more similar to SARS. 4 First among the important unknowns about COVID-19 is the case fatality rate (CFR), which requires information on the denominator that defines the number infected. We are unaware of any completed large-scale serology surveys to detect specific antibodies to COVID-19. Best estimates suggest a CFR for COVID-19 of about 0·3–1%, 4 which is higher than the order of 0·1% CFR for a moderate influenza A season. 5 The second unknown is the whether infectiousness starts before onset of symptoms. The incubation period for COVID-19 is about 5–6 days.4, 6 Combining this time with a similar length serial interval suggests there might be considerable presymptomatic infectiousness (appendix 1). For reference, influenza A has a presymptomatic infectiousness of about 1–2 days, whereas SARS had little or no presymptomatic infectiousness. 7 There have been few clinical studies to measure COVID-19 viraemia and how it changes over time in individuals. In one study of 17 patients with COVID-19, peak viraemia seems to be at the end of the incubation period, 8 pointing to the possibility that viraemia might be high enough to trigger transmission for 1–2 days before onset of symptoms. If these patterns are verified by more extensive clinical virological studies, COVID-19 would be expected to be more like influenza A than SARS. For SARS, peak infectiousness took place many days after first symptoms, hence the success of quarantine of patients with SARS soon after symptoms started 7 and the lack of success for this measure for influenza A and possibly for COVID-19. The third uncertainty is whether there are a large number of asymptomatic cases of COVID-19. Estimates suggest that about 80% of people with COVID-19 have mild or asymptomatic disease, 14% have severe disease, and 6% are critically ill, 9 implying that symptom-based control is unlikely to be sufficient unless these cases are only lightly infectious. The fourth uncertainty is the duration of the infectious period for COVID-19. The infectious period is typically short for influenza A, but it seems long for COVID-19 on the basis of the few available clinical virological studies, perhaps lasting for 10 days or more after the incubation period. 8 The reports of a few super-spreading events are a routine feature of all infectious diseases and should not be overinterpreted. 10 What do these comparisons with influenza A and SARS imply for the COVID-19 epidemic and its control? First, we think that the epidemic in any given country will initially spread more slowly than is typical for a new influenza A strain. COVID-19 had a doubling time in China of about 4–5 days in the early phases. 3 Second, the COVID-19 epidemic could be more drawn out than seasonal influenza A, which has relevance for its potential economic impact. Third, the effect of seasons on transmission of COVID-19 is unknown; 11 however, with an R0 of 2–3, the warm months of summer in the northern hemisphere might not necessarily reduce transmission below the value of unity as they do for influenza A, which typically has an R0 of around 1·1–1·5. 12 Closely linked to these factors and their epidemiological determinants is the impact of different mitigation policies on the course of the COVID-19 epidemic. A key issue for epidemiologists is helping policy makers decide the main objectives of mitigation—eg, minimising morbidity and associated mortality, avoiding an epidemic peak that overwhelms health-care services, keeping the effects on the economy within manageable levels, and flattening the epidemic curve to wait for vaccine development and manufacture on scale and antiviral drug therapies. Such mitigation objectives are difficult to achieve by the same interventions, so choices must be made about priorities. 13 For COVID-19, the potential economic impact of self-isolation or mandated quarantine could be substantial, as occurred in China. No vaccine or effective antiviral drug is likely to be available soon. Vaccine development is underway, but the key issues are not if a vaccine can be developed but where phase 3 trials will be done and who will manufacture vaccine at scale. The number of cases of COVID-19 are falling quickly in China, 4 but a site for phase 3 vaccine trials needs to be in a location where there is ongoing transmission of the disease. Manufacturing at scale requires one or more of the big vaccine manufacturers to take up the challenge and work closely with the biotechnology companies who are developing vaccine candidates. This process will take time and we are probably a least 1 year to 18 months away from substantial vaccine production. So what is left at present for mitigation is voluntary plus mandated quarantine, stopping mass gatherings, closure of educational institutes or places of work where infection has been identified, and isolation of households, towns, or cities. Some of the lessons from analyses of influenza A apply for COVID-19, but there are also differences. Social distancing measures reduce the value of the effective reproduction number R. With an early epidemic value of R0 of 2·5, social distancing would have to reduce transmission by about 60% or less, if the intrinsic transmission potential declines in the warm summer months in the northern hemisphere. This reduction is a big ask, but it did happen in China. School closure, a major pillar of the response to pandemic influenza A, 14 is unlikely to be effective given the apparent low rate of infection among children, although data are scarce. Avoiding large gatherings of people will reduce the number of super-spreading events; however, if prolonged contact is required for transmission, this measure might only reduce a small proportion of transmissions. Therefore, broader-scale social distancing is likely to be needed, as was put in place in China. This measure prevents transmission from symptomatic and non-symptomatic cases, hence flattening the epidemic and pushing the peak further into the future. Broader-scale social distancing provides time for the health services to treat cases and increase capacity, and, in the longer term, for vaccines and treatments to be developed. Containment could be targeted to particular areas, schools, or mass gatherings. This approach underway in northern Italy will provide valuable data on the effectiveness of such measures. The greater the reduction in transmission, the longer and flatter the epidemic curve (figure ), with the risk of resurgence when interventions are lifted perhaps to mitigate economic impact. Figure Illustrative simulations of a transmission model of COVID-19 A baseline simulation with case isolation only (red); a simulation with social distancing in place throughout the epidemic, flattening the curve (green), and a simulation with more effective social distancing in place for a limited period only, typically followed by a resurgent epidemic when social distancing is halted (blue). These are not quantitative predictions but robust qualitative illustrations for a range of model choices. The key epidemiological issues that determine the impact of social distancing measures are what proportion of infected individuals have mild symptoms and whether these individuals will self-isolate and to what effectiveness; how quickly symptomatic individuals take to isolate themselves after the onset of symptoms; and the duration of any non-symptomatic infectious period before clear symptoms occur with the linked issue of how transmissible COVID-19 is during this phase. Individual behaviour will be crucial to control the spread of COVID-19. Personal, rather than government action, in western democracies might be the most important issue. Early self-isolation, seeking medical advice remotely unless symptoms are severe, and social distancing are key. Government actions to ban mass gatherings are important, as are good diagnostic facilities and remotely accessed health advice, together with specialised treatment for people with severe disease. Isolating towns or even cities is not yet part of the UK Government action plan. 15 This plan is light on detail, given the early stages of the COVID-19 epidemic and the many uncertainties, but it outlines four phases of action entitled contain, delay, research, and mitigate. 15 The UK has just moved from contain to delay, which aims to flatten the epidemic and lower peak morbidity and mortality. If measures are relaxed after a few months to avoid severe economic impact, a further peak is likely to occur in the autumn (figure). Italy, South Korea, Japan, and Iran are at the mitigate phase and trying to provide the best care possible for a rapidly growing number of people with COVID-19. The known epidemiological characteristics of COVID-19 point to urgent priorities. Shortening the time from symptom onset to isolation is vital as it will reduce transmission and is likely to slow the epidemic (appendices 2, 3) However, strategies are also needed for reducing household transmission, supporting home treatment and diagnosis, and dealing with the economic consequences of absence from work. Peak demand for health services could still be high and the extent and duration of presymptomatic or asymptomatic transmission—if this turns out to be a feature of COVID-19 infection—will determine the success of this strategy. 16 Contact tracing is of high importance in the early stages to contain spread, and model-based estimates suggest, with an R0 value of 2·5, that about 70% of contacts will have to be successfully traced to control early spread. 17 Analysis of individual contact patterns suggests that contact tracing can be a successful strategy in the early stages of an outbreak, but that the logistics of timely tracing on average 36 contacts per case will be challenging. 17 Super-spreading events are inevitable, and could overwhelm the contact tracing system, leading to the need for broader-scale social distancing interventions. Data from China, South Korea, Italy, and Iran suggest that the CFR increases sharply with age and is higher in people with COVID-19 and underlying comorbidities. 18 Targeted social distancing for these groups could be the most effective way to reduce morbidity and concomitant mortality. During the outbreak of Ebola virus disease in west Africa in 2014–16, deaths from other causes increased because of a saturated health-care system and deaths of health-care workers. 19 These events underline the importance of enhanced support for health-care infrastructure and effective procedures for protecting staff from infection. In northern countries, there is speculation that changing contact patterns and warmer weather might slow the spread of the virus in the summer. 11 With an R0 of 2·5 or higher, reductions in transmission by social distancing would have to be large; and much of the changes in transmission of pandemic influenza in the summer of 2009 within Europe were thought to be due to school closures, but children are not thought to be driving transmission of COVID-19. Data from the southern hemisphere will assist in evaluating how much seasonality will influence COVID-19 transmission. Model-based predictions can help policy makers make the right decisions in a timely way, even with the uncertainties about COVID-19. Indicating what level of transmission reduction is required for social distancing interventions to mitigate the epidemic is a key activity (figure). However, it is easy to suggest a 60% reduction in transmission will do it or quarantining within 1 day from symptom onset will control transmission, but it is unclear what communication strategies or social distancing actions individuals and governments must put in place to achieve these desired outcomes. A degree of pragmatism will be needed for the implementation of social distancing and quarantine measures. Ongoing data collection and epidemiological analysis are therefore essential parts of assessing the impacts of mitigation strategies, alongside clinical research on how to best manage seriously ill patients with COVID-19. There are difficult decisions ahead for governments. How individuals respond to advice on how best to prevent transmission will be as important as government actions, if not more important. Government communication strategies to keep the public informed of how best to avoid infection are vital, as is extra support to manage the economic downturn.

SARS-CoV-2 is a new Coronavirus, with first infections detected in humans late in 2019. The emergence of SARS-CoV-2 has led to a large outbreak in China and is currently causing outbreaks in many countries. The disease spectrum ranges from uncomplicated upper respiratory tract infections to severe viral pneumonia with multiorgan failure and death. It can be transmitted by droplets from asymptomatic or oligosymptomatic patients and possibly through aerosols in health care environments. The route of transmission and the spectrum of disease (COVID-19) has motivated many researchers to use models of influenza outbreaks or pandemics to forecast outbreaks of SARS-CoV-2 by analogy. The epicenter of China’s outbreak has been Wuhan and the Hubei province. The Chinese government has restricted travel from and to Hubei province and has implemented a number of measures to contain the outbreak. Meanwhile, the number of new cases per day in China is falling. A WHO mission has visited China and Wuhan to report on the outbreak. They corroborated the outbreak dynamic and case count reported by the Chinese government [2]. The Chinese success-estimating an upper limit to the attack rate in Hubei province Intensive public health interventions have been employed, and some experts expect the outbreak to end as early as in April. The bundle of public health interventions has included intensive case and contact tracking, isolation of moderately ill patients in containment centers, social distancing, and shutting down public life of a whole province and many major cities outside Hubei. Just how effective the outbreak seems to have been contained is astonishing. Publicly available data can be employed to estimate the attack rate of the COVID-19 outbreak in China. There are two datasets with a very different picture of the same epidemic caused by the same virus [3]. Data from Hubei (roughly 80% of the China outbreak with a focus on severe cases and high case fatality rate (CFR), currently cumulatively estimated to be around 4%). Data from China outside Hubei province with a probably much better coverage of the whole epidemic due to active case finding. With steadily declining case numbers and numbers of new deaths also declining in the second group, the case fatality rate in China outside Hubei province is stabilizing around 0.8% (Fig. 1). If we put these two datasets parallelly we could cautiously (to be on the safe side for an upper limit estimate) assume, that up to 50% of cases might still be missed outside Hubei province with the consequence of a lower case fatality rate, because severe cases are unlikely to be missed (Fig. 2). Fig. 1 COVID-19 cases/death in China by day. Daily cases and deaths in China, case count for February 13 truncated (change of case definition, 16,119 cases in the part retrospectively reported) (adapted from 3) Fig. 2 CFR Hubei and China outside Hubei. CFR in Hubei (orange) and in China outside Hubei province (black), calculated from daily cumulative case and deaths numbers. Horizontal lines indicate current status (adapted from 3) The second cautious assumption would be that the current caseload in Hubei province represents only 2/3 of the final caseload, putting the total number to approximately 100.000 cases (with the current clinical characteristics). To set this parallelly with the epidemic outside Hubei province, we would have to multiply this case count by 5–10 (five if the data outside Hubei reflect all cases, ten for under reporting of 50%). Thus, the number of cases in the Hubei outbreak could be estimated as between 500.000–1.000.0000. With a population of 57 million people in Hubei province, the attack rate would be below 2%. How is COVID-19 different from Influenza? This estimate of an upper limit of 2% is considerably lower than previous forecasts and estimates in analogy to influenza pandemics or outbreaks (Table 1). Compared to influenza outbreaks the attack rate and burden of disease in children have been much lower and the secondary household attack rate has also been low (Table 1). This is in sharp contrast to observations of a very rapid spread of the virus in confined situations as prisons or cruise ships and the high rate of healthcare-associated infections. Several hypotheses will have to be explored to answer at least some of the questions: (1) initial estimates of R0 might have been biased by clusters of effective transmission (“super-spreaders”), (2) public health measures might be more effective to reduce Rt in SARS-CoV-2 than in influenza outbreaks and (3) whether there is a threshold of prevalent cases in the community, which if reached, the epidemic can be effectively contained only with drastic measures. Table 1 Attack rates of pandemic or seasonal influenza and COVID-19 in Hubei province [1, 2] Influenza 1957 Influenza 1968 Influenza 2009 Influenza 1977–8 COVID-19, Hubei 2020 Community attack rate confirmed cases 18.5–26.8% 15% 17,5 2.2–31%  < 2% Secondary household contact attack rate 8.4–23%a 20%a 4–6% 16% 3–10% aNot laboratory confirmed Conclusions and lessons First, the WHO report is very good news for the people in Hubei province and all health care workers involved. Second, the success of the interventions demonstrates that strict and rapid response to an emerging epidemic can halt the spread of a new virus. But there are also some sobering insights looking at the current situation outside China and the messages in the WHO report. China’s success might not be the end of their outbreak. An attack rate as low as 2% could cause a second wave rapidly, because the community level of immunity is still low. Furthermore, the virus has been imported in a large number of countries, which are facing difficult choices regarding public health measures and challenges to their health care system. The outbreak in Hubei province has shown how much harm a newly emerging respiratory virus can cause. Infections in confined spaces, such as prisons or cruise ships, can rapidly spread, complications can be severe and health care-associated transmission poses a risk for HCWs and other patients. Health care workers from all over China have come to Hubei to help and have been doing excellent and very trying work in treating the large number of patients with a very high number of hospitalized and critically ill patients. Their growing experience in patient care is also reflected in the declining case fatality rate and the declining number of healthcare-associated transmission over the time course of the outbreak. The resulting publications of clinical data will be very helpful for patient care outside Hubei province and as clinicians we will profit immensely from those. But we do not know how the situation in Hubei might have been if the virus had spread early to other metropolitan centers in China. The workload for healthcare workers would have been multiplied, a collapse of the healthcare system would have been possible and the death toll would have been very high. In the light of these consequences, any public health intervention seems to be a better option. So, despite the good news from China, the work is far from over. Outside China, we face enormous challenges: (1) to effectively contain the current and future outbreaks worldwide, and (2) to treat infected patients effectively and safely. Looking at the Chinese experience, we hope that public health measures outside China will be as rapid and effective as in China. We should implement those before reaching a critical threshold of infections.