Global S&T Development Trend Analysis Platform of Resources and Environment
| DOI | 10.1126/science.abg0842 |
| Networks of SARS-CoV-2 transmission | |
| Muge Cevik; Stefan D. Baral | |
| 2021-07-09 | |
| 发表期刊 | Science
 |
| 出版年 | 2021 |
| 英文摘要 | The basic reproduction number, R (the number of infections caused by a case in a homogeneously susceptible population), for a particular infection is dependent on the epidemiological triad of the biological characteristics of the pathogen, the environment, and the characteristics of the population ([ 1 ][1]). Even for diseases with similar transmission characteristics, R varies by population owing to differential opportunities for onward transmission according to the contact patterns and the size of the transmission network of an infected individual ([ 1 ][1]). Although transmission can happen in many settings, some factors facilitate a greater risk of infection because of compounded risks often driven by network dynamics (frequent contacts, close proximity, and prolonged contact) and structural-level determinants (such as poverty, occupation, and household size) ([ 2 ][2]–[ 4 ][3]). Understanding drivers of transmission risks and heterogeneity could be used to improve modeling and guide population- and setting-specific mitigation strategies.
In the context of an epidemic, although each contact carries a risk of acquiring an infection, real-world social networks are complex, often exhibiting extreme heterogeneity in the number of contacts, which have large-scale effects on the spread of infection ([ 5 ][4]). In infectious diseases, the population attributable fraction (PAF) represents the total contribution of a risk that could be averted if that risk were avoided ([ 6 ][5]). Even for lower-risk exposures, the PAF could increase with higher exposure frequency mediated through greater numbers of contacts ([ 2 ][2], [ 6 ][5]). For example, the risk of infection depends on the likelihood of transmission within a particular environment and the frequency at which people visit that setting. At an individual level, settings that are associated with higher-risk factors and visited frequently are likely to pose a higher risk of infection and contribute substantially to cumulative infections than those that may have a higher risk but are visited infrequently. This could mean that a small relative risk of a high-frequency exposure can drive the PAF, suggesting that public health interventions could prioritize resources to eliminate a small risk among many.
However, in reality, risk factors concentrate among the relatively few who have disproportionately higher exposure and onward transmission risks ([ 2 ][2], [ 7 ][6]). This individual heterogeneity is evident in data, which consistently indicate higher risks of infection due to higher frequency of exposure and multiple contacts (see the figure). In many countries, those working in low-paid and public-facing jobs had the highest risk of being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ([ 4 ][3]). Long-term–care facilities such as nursing homes, homeless shelters, and prisons, as well as workplaces such as meat-packing plants, have been associated with large-scale outbreaks of COVID-19, which were then linked to sustained widespread community transmission ([ 2 ][2], [ 8 ][7]). These settings often represent environments where risks for infection are compounded and multiple transmission networks intersect ([ 7 ][6]). There is also a clear intersection of COVID-19 risk and socioeconomic inequities, given the network effects of occupation, crowded housing, job insecurity, and poverty ([ 2 ][2], [ 4 ][3]).
The disproportionate risks associated with network dynamics have also led to differential disease burden ([ 4 ][3], [ 9 ][8]). According to an analysis from Scotland, patients living in areas with the greatest socioeconomic deprivation had a higher frequency of intensive-care admission and higher COVID-19–related mortality ([ 10 ][9]). Health care units in the most deprived areas also operated over capacity for a more prolonged period ([ 10 ][9]). In a US study, those working in food and agriculture, transportation or logistics, manufacturing, health services, and retail had significantly increased excess mortality related to COVID-19 ([ 9 ][8]). Moreover, differential living and working conditions often manifest as racial disparities because of structural racism. An analysis by the Office for National Statistics highlights the finding that occupations in the UK with higher COVID-19–related death rates include health and social care workers, security guards, drivers, construction workers, cleaners, and sales and retail assistants, which are occupations that also feature higher proportions of minority ethnic groups ([ 4 ][3]). For most occupational categories, the risk ratios comparing mortality during the pandemic with that during nonpandemic time were higher in nonwhite ethnic groups ([ 4 ][3], [ 9 ][8]).
In addition to heterogeneity in risk of exposure and disease burden, there are also heterogeneities in risk of onward transmission. Per-contact, direct onward transmission risks are driven by multiple factors, including closeness of social interactions, symptom status, the severity of illness, environment, and time of exposure ([ 2 ][2], [ 6 ][5]). For example, the average per-contact risk is lowest for community exposures, intermediate for social and extended-family contacts, and highest in the household ([ 11 ][10]). Transmission risk is lower when the index case is asymptomatic, increasing with symptom severity ([ 12 ][11]). Indirect onward transmission risks or the total number of downstream infections that stem from an individual over multiple chains of transmission represent important contributions to the overall PAF driven by the size of the transmission networks associated with living and working conditions ([ 4 ][3], [ 7 ][6], [ 13 ][12]).
Although some high-frequency contacts are driven by social gatherings, which are modifiable with education and enforcement, most high-risk exposures represent nonmodifiable risks due to living and working conditions ([ 2 ][2], [ 3 ][13], [ 7 ][6]). Therefore, risk factors that are nonmodifiable in the short term are likely to represent a much larger PAF than those modifiable by individual choices about social contact. Specifically, the onward transmission risks from someone who can work from home and has enough space for self-isolation, even if they are infected, may be minimal; but the PAF will be higher for someone with a large network associated with working and living conditions (see the figure).
There is now international consensus that those living in the most economically deprived neighborhoods and largest households have an increased risk of infection and disease burden ([ 3 ][13], [ 4 ][3]). In addition, inequities further concentrate risk through connections between networks. In Toronto, long-term–care staff diagnosed with COVID-19 were disproportionately more likely to reside in neighborhoods with the highest infection rates, which are also the most economically deprived and ethnically concentrated ([ 14 ][14]). In a COVID-19 outbreak investigation among large industries in Ontario, one-third of cases linked to workplace outbreaks spilled over to households, further increasing the burden of illness ([ 15 ][15]). Therefore, structural conditions that affect an individual's network and exposure risk are likely far more predictive than individual choices in determining whether the infection will be a terminal event or lead to multiple downstream infections. Thus, comprehensively addressing the needs of a few with disproportionate risks can avert more downstream infections than eliminating a small risk among many.
How can public health strategies address individual heterogeneity and differential infection risk? Early in the pandemic, there was an assumption of relative homogeneity in the risks of infection and the potential impact of interventions across the population. This was included in modeling to inform public health approaches. Compartmental models, which divide populations into distinct sections and assume that individuals in these groups have the same characteristics, are mostly used to model COVID-19 cases and the impact of interventions. However, they infrequently integrate the effects of differential population mixing, socioeconomic factors, and networks across compartment effects. It is now clear that individual heterogeneity has large-scale effects on disparities seen in the risk of infection and disease burden, which is confirmed in network-based disease modeling ([ 1 ][1], [ 6 ][5], [ 7 ][6], [ 11 ][10]). Public health policies implemented based on the assumption of equal risk of acquisition and transmission across all socioeconomic groups, ages, and occupations left certain communities exposed to a higher risk of infection, resulting in differential burdens of disease ([ 1 ][1]–[ 3 ][13], [ 7 ][6]). Leveraging network heterogeneity in infectious disease models may better demonstrate these differential risks observed in real-life epidemiological analyses and the benefits of prioritizing intensive and targeted interventions to those with differential risks, given the potential for larger numbers of averted downstream infections.
![Figure][16]
Downstream infection risks vary according to network patterns
Case A depicts a person with a small network, who can work from home and self-isolate if needed. Case B represents a person who works in a public-facing job or in an unsafe workplace and lives in a multigenerational or large household. Overall risk of exposure and onward transmission risk differ substantially between these two individuals, representing a disproportionately high transmission chain in case B. Intervention strategies should focus on breaking chains of downstream transmission.
GRAPHIC: K. FRANKLIN/ SCIENCE ; (ILLUSTRATIONS OF PEOPLE) PROPUBLICA'S WEEPEOPLE FONT
The intersection between direct and indirect onward transmission risks reinforces the need for effective and pragmatic strategies to break chains of transmission, especially in people at high risk of infection. Policy interventions should consider the overall number of contacts that a person has and, subsequently, downstream infections averted based on differential impacts in different communities ([ 13 ][12]). For example, people living in multigenerational households, serving in high-exposure occupations, and residing in densely populated communities could be prioritized for temporary housing support, assurance of employee benefits such as paid leave, and vaccine outreach services. Considering this differential impact, targeted interventions through network-adaptive resource-based interventions could be leveraged according to individual and network-level needs. Such an adaptive approach could inform modeling and prioritize specific resource-based intervention strategies, including testing aligned with lived realities, housing support if insufficient space to isolate, and paid leave from work to support quarantine and isolation, combined with outreach testing and infection prevention and control support in workplaces. In addition, network-adaptive vaccination strategies prioritize those with large networks based on contact heterogeneity. For example, looking at vaccination rates in England by deprivation, vaccination coverage is clearly lower in more deprived areas, where the risk of infection and disease burden is higher. Although this may be due to multiple reasons, including lack of access to care and inability to take time off work, there is a need for areas of high and enduring transmission risk to accelerate vaccination to match the increased risks of infection and onward transmission.
The focus of COVID-19 response strategies has often been on behavior change as a primary means of decreasing contact networks and thus transmission chains. However, contact patterns are driven, in large part, by socioeconomic inequities and structural racism and are nonmodifiable at the individual level in the absence of specific support. Thus, nonadaptive public health interventions fail to address individual heterogeneities and have left socioeconomically marginalized communities at risk of infection, death, and economic hardship. There is a risk that lower vaccine uptake among these communities could perpetuate existing inequalities. Therefore, it is vital that community-led vaccine delivery strategies are strengthened.
The disparities that have defined COVID-19 epidemiology could have been readily predictable given historical data on pandemics. The next respiratory pandemic will also be defined by similar disparities. Using network-driven strategies to inform rapidly emergent epidemic responses represents an evidence-based and equitable path forward where the aim is to invest more to prevent infections in a person with disproportionate risks because disease burden and downstream infection risks vary substantially ([ 6 ][5], [ 7 ][6], [ 13 ][12]).
1. [↵][17]1. T. D. Hollingsworth
, J. Public Health Policy 30, 328 (2009).
[OpenUrl][18][CrossRef][19][PubMed][20]
2. [↵][21]1. M. Cevik,
2. J. L. Marcus,
3. C. Buckee,
4. T. C. Smith
, Clin. Infect. Dis. 10.1093/cid/ciaa1442 (2020).
3. [↵][22]1. M. E. Sundaram et al
., medRxiv 10.1101/2020.11.09.20223792 (2020).
4. [↵][23]Public Health England (PHE) Transmission Group, “Factors contributing to risk of SARS-CoV2 transmission in various settings” (PHE, 2020); |
| 领域 | 气候变化 ; 资源环境 |
| URL | 查看原文 |
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| 文献类型 | 期刊论文 |
| 条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/334237 |
| 专题 | 气候变化 资源环境科学 |
| 推荐引用方式 GB/T 7714 | Muge Cevik,Stefan D. Baral. Networks of SARS-CoV-2 transmission[J]. Science,2021. |
| APA | Muge Cevik,&Stefan D. Baral.(2021).Networks of SARS-CoV-2 transmission.Science. |
| MLA | Muge Cevik,et al."Networks of SARS-CoV-2 transmission".Science (2021). |
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