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Multi-route respiratory infection: when a transmission route may dominate

11 Apr 2020-medRxiv (Cold Spring Harbor Laboratory Press)-

TL;DR: A detailed mathematical model is developed to investigate the relative contributions of different transmission routes to a multi-route transmitted respiratory infection and provides a basis for predicting the impact of individual level intervention methods such as increasing close-contact distance and wearing protective masks.
Abstract: The exact transmission route of many respiratory infectious diseases remains a subject for debate to date. The relative contribution ratio of each transmission route is largely undetermined, which is affected by environmental conditions, human behavior, the host and the microorganism. In this study, a detailed mathematical model is developed to investigate the relative contributions of different transmission routes to a multi-route transmitted respiratory infection. It is illustrated that all transmission routes can dominate the total transmission risk under different scenarios. Influential parameters considered include dose-response rate of different routes, droplet governing size that determines virus content in droplets, exposure distance, and virus dose transported to the hand of infector. Our multi-route transmission model provides a comprehensive but straightforward method to evaluate the transmission efficiency of different transmission routes of respiratory diseases and provides a basis for predicting the impact of individual level intervention methods such as increasing close-contact distance and wearing protective masks. (Word count: 153) Highlights A multi-route transmission model is developed by considering evaporation and motion of respiratory droplets with the respiratory jet and consequent exposure of the susceptible. We have illustrated that each transmission route may dominate during the influenza transmission, and the influential factors are revealed. The short-range airborne route and infection caused by direct inhalation of medium droplets are highlighted.
Topics:Β Respiratory infectionΒ (63%)

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1
Multi-route respiratory infection: when a transmission route
may dominate
Caroline X. Gao
1,2.3
, Yuguo Li
4,6
, Jianjian Wei
5,6
*, Sue Cotton
1,3
, Matthew Hamilton
1
,
Lei Wang
5
, and Benjamin J. Cowling
7
1
Centre for Youth Mental Health, University of Melbourne, Parkville, VIC 3052,
Australia
2
School of Public Health and Preventive Medicine, Monash University, 553 St Kilda
Rd, Melbourne, VIC 3004, Australia.
3
Orygen, Parkville, VIC 3052, Australia
4
Department of Mechanical Engineering, The University of Hong Kong, Pokfulam
Road, Hong Kong SAR, China, 999077
5
Institute of Refrigeration and Cryogenics, and Key Laboratory of Refrigeration and
Cryogenic Technology of Zhejiang Province, Zhejiang University, Hangzhou, China,
310000
6
HKU Shenzhen Institute of Research and Innovation, Shenzhen, China, 518053
7
School of Public Health, The University of Hong Kong, Pokfulam Road, Hong Kong
SAR, China, 999077
*Corresponding author:
Jianjian Wei
Institute of Refrigeration and Cryogenics
Zhejiang University
Hangzhou, China, 310000
Tel: (86) 571-87953944, Email: weijzju@zju.edu.cn
Word count : 3728
All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted April 11, 2020. ; https://doi.org/10.1101/2020.04.06.20055228doi: medRxiv preprint
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.

2
Multi-route respiratory infection: when a transmission route
may dominate
Abstract
The exact transmission route of many respiratory infectious diseases remains a subject
for debate to date. The relative contribution ratio of each transmission route is largely
undetermined, which is affected by environmental conditions, human behavior, the
host and the microorganism. In this study, a detailed mathematical model is developed
to investigate the relative contributions of different transmission routes to a multi-
route transmitted respiratory infection. It is illustrated that all transmission routes can
dominate the total transmission risk under different scenarios. Influential parameters
considered include dose-response rate of different routes, droplet governing size that
determines virus content in droplets, exposure distance, and virus dose transported to
the hand of infector. Our multi-route transmission model provides a comprehensive
but straightforward method to evaluate the transmission efficiency of different
transmission routes of respiratory diseases and provides a basis for predicting the
impact of individual level intervention methods such as increasing close-contact
distance and wearing protective masks. (Word count: 153)
Keywords
Multi-route transmission, short-range airborne route, long-range airborne route,
building ventilation, respiratory infection, influenza
All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted April 11, 2020. ; https://doi.org/10.1101/2020.04.06.20055228doi: medRxiv preprint

3
Highlights
1. A multi-route transmission model is developed by considering evaporation and
motion of respiratory droplets with the respiratory jet and consequent exposure of
the susceptible.
2. We have illustrated that each transmission route may dominate during the
influenza transmission, and the influential factors are revealed.
3. The short-range airborne route and infection caused by direct inhalation of
medium droplets are highlighted.
Introduction
The 2003 Severe Acute Respiratory Syndrome (SARS) epidemics, the 2009 H1N1
influenza (Swine Flu) pandemic, the 2015 Middle East Respiratory Syndrome –
coronavirus (MERS-CoV) epidemics and the ongoing novel human coronavirus
(SARS-CoV-2) global pandemic have all highlighted the importance of studying the
transmission mechanism of respiratory infectious diseases (1-4).
Respiratory diseases are often simply assumed to be transmitted via β€œclose contact”;
however, the transmission mechanisms are complex involving more than one
transmission route including direct or indirect contact, large droplet, and airborne
routes (5-9). There are many physical (respiratory particles and droplets generation),
virological (viral loading, survival, location of virus receptor, etc.), behavioral
(exposure distance, frequency of handshaking and surface touching, etc.) and
environmental factors (temperature, humidity, ventilation, etc.) that affect the
transmission (8, 10).
All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted April 11, 2020. ; https://doi.org/10.1101/2020.04.06.20055228doi: medRxiv preprint

4
Hence, respiratory infections may show various characteristics under different contact
scenarios. For example, airborne transmission was identified as played the leading
role in an influenza outbreak on a commercial aircraft in 1977 in Alaska (11).
Conversely, in a H1N1 outbreak in a tour group in China, close contact was the most
correlated factor with the transmission (12). Conflicting evidence for transmission
routes, like these two cases, are prevalent for almost all respiratory infectious diseases
(8). Failure in understanding the complex multi-route transmission mechanisms leads
to recommendations of more conservative intervention methods such as keep a
distance rather than increasing ventilation and wearing masks. The consequences of
more conservative interventions can be catastrophic such as the global pandemic of
SARS-CoV-2 outbreak (13-15).
However, understanding of multi-route transmission is by no means an easy task.
Findings from animal challenge models are difficult to extrapolate to human
transmission (16). Human challenge models are expensive and often unethical (17).
Observational studies of existing outbreaks often fail to capture important time
relevant evidence. A more feasible approach is to use mathematical models to
describe the multi-route transmission using known parameters such as droplet
generation rate, virus shedding rate, and virus survival rates. A few mathematical
studies have developed multi-route transmission models such as by Nicas and Jones
(18), Atkinson and Wein (19) and Spicknall and colleagues (20). However, many
critical factors, such as evaporation of respiratory droplets, travelling of the large
droplets in the respiratory jet, pulmonary deposition, dose-response rate for different
route were not fully evaluated in these models, which may underestimate the role of
smaller droplets.
All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted April 11, 2020. ; https://doi.org/10.1101/2020.04.06.20055228doi: medRxiv preprint

5
In this paper, we first provide comprehensive definitions of transmission routes which
incorporates the underlying physical principles of multi-route transmission. Second,
we establish a more advanced simulation model to describe the infection via different
routes considering the physical components of particles and droplets movement,
differences in possible viral dose-effect as well as human-behavior factors such as
touching mouth and nose. We use influenza as an example due to the large number of
related studies for extracting modelling parameters. Using the model, we aim to
challenge the traditional dichotomous thinking of close contact transmission vs
airborne (aerosol) transmission via highlighting the scenarios under which each
transmission route may dominate and how environmental and behavior factors
interact in the transmission mechanism.
Methodology
Transmission routes definitions
Traditional definitions for transmission routes include the airborne route (also referred
as aerosol transmission) (21), large droplet route, and contact route (6, 22). However,
such definitions are somewhat ambiguous. Firstly, the cut-off size of droplets for
airborne transmission has always been controversial (8, 22, 23). The droplet nuclei,
first defined by Wells (24), refers to the residues of droplets after complete
evaporation. Centers for Disease Control and Prevention (CDC) defined the cut-off
size of 5 Β΅m for airborne transmission (25), and the threshold distance for airborne
transmission is defined by the World Health Organization (WHO) as 1.0 m (26).
However, it is known that droplet nuclei over 5 Β΅m may also easily suspend and
disperse over 1.0 m to cause transmission of respiratory disease, depending on the
All rights reserved. No reuse allowed without permission.
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprintthis version posted April 11, 2020. ; https://doi.org/10.1101/2020.04.06.20055228doi: medRxiv preprint

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Cites background from "Multi-route respiratory infection: ..."

  • ...…and resource-poor communities, highrisk ethnic minorities, children in close contact with high-risk category patients, and health-care providers serving these patients (Griffith and Kerr, 1996), include alcoholism (Lawn and Zumla, 2011) and diabetes mellitus (threefold increase) (Restrepo, 2007)....

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Te Faye Yap1, Colter J. Decker1, Daniel J. Preston1β€’Institutions (1)
TL;DR: The modeling framework presented here provides insight into the independent effects of mean temperature and DTR on virus lifetime, and a significant impact on transmission rate is expected, especially for viruses that pose a high risk of fomite-mediated transmission.
Abstract: Epidemiological studies based on statistical methods indicate inverse correlations between virus lifetime and both (i) daily mean temperature and (ii) diurnal temperature range (DTR). While thermodynamic models have been used to predict the effect of constant-temperature surroundings on virus inactivation rate, the relationship between virus lifetime and DTR has not been explained using first principles. Here, we model the inactivation of viruses based on temperature-dependent chemical kinetics with a time-varying temperature profile to account for the daily mean temperature and DTR simultaneously. The exponential Arrhenius relationship governing the rate of virus inactivation causes fluctuations above the daily mean temperature during daytime to increase the instantaneous rate of inactivation by a much greater magnitude than the corresponding decrease in inactivation rate during nighttime. This asymmetric behavior results in shorter predicted virus lifetimes when considering DTR and consequently reveals a potential physical mechanism for the inverse correlation observed between the number of cases and DTR reported in statistical epidemiological studies. In light of the ongoing COVID-19 pandemic, a case study on the effect of daily mean temperature and DTR on the lifetime of SARS-CoV-2 was performed for the five most populous cities in the United States. In Los Angeles, where mean monthly temperature fluctuations are low (DTR β‰ˆ 7 Β°C), accounting for DTR decreases predicted SARS-CoV-2 lifetimes by only 10%; conversely, accounting for DTR for a similar mean temperature but larger mean monthly temperature fluctuations in Phoenix (DTR β‰ˆ 15 Β°C) decreases predicted lifetimes by 50%. The modeling framework presented here provides insight into the independent effects of mean temperature and DTR on virus lifetime, and a significant impact on transmission rate is expected, especially for viruses that pose a high risk of fomite-mediated transmission.

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References
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Catrin Sohrabi1, Zaid Alsafi2, Niamh O'Neill1, M.N.I. Khan2Β  +4 moreβ€’Institutions (4)
TL;DR: Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide.
Abstract: An unprecedented outbreak of pneumonia of unknown aetiology in Wuhan City, Hubei province in China emerged in December 2019. A novel coronavirus was identified as the causative agent and was subsequently termed COVID-19 by the World Health Organization (WHO). Considered a relative of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), COVID-19 is caused by a betacoronavirus named SARS-CoV-2 that affects the lower respiratory tract and manifests as pneumonia in humans. Despite rigorous global containment and quarantine efforts, the incidence of COVID-19 continues to rise, with 90,870 laboratory-confirmed cases and over 3,000 deaths worldwide. In response to this global outbreak, we summarise the current state of knowledge surrounding COVID-19.

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"Multi-route respiratory infection: ..." refers background in this paper

  • ...The consequences of more conservative interventions can be catastrophic such as the global pandemic of SARS-CoV-2 outbreak (13) (14) (15) ....

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Journal Articleβ€’DOIβ€’
Julien Riou1, Christian L. Althaus1β€’Institutions (1)
TL;DR: Transmission characteristics appear to be of similar magnitude to severe acute respiratory syndrome-related coronavirus (SARS-CoV) and pandemic influenza, indicating a risk of global spread.
Abstract: Since December 2019, China has been experiencing a large outbreak of a novel coronavirus (2019-nCoV) which can cause respiratory disease and severe pneumonia. We estimated the basic reproduction number R0 of 2019-nCoV to be around 2.2 (90% high density interval: 1.4–3.8), indicating the potential for sustained human-to-human transmission. Transmission characteristics appear to be of similar magnitude to severe acute respiratory syndrome-related coronavirus (SARS-CoV) and pandemic influenza, indicating a risk of global spread.

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Fabrice Carrat1, Fabrice Carrat2, Elisabeta Vergu2, Elisabeta Vergu3Β  +8 moreβ€’Institutions (5)
TL;DR: Prior expert opinion on the duration of viral shedding or the frequency of asymptomatic influenza infection is confirmed, prior knowledge on the dynamics of viral shed and symptoms is extended, and original results on the frequencyof respiratory symptoms or fever are provided.
Abstract: The dynamics of viral shedding and symptoms following influenza virus infection are key factors when considering epidemic control measures. The authors reviewed published studies describing the course of influenza virus infection in placebo-treated and untreated volunteers challenged with wild-type influenza virus. A total of 56 different studies with 1,280 healthy participants were considered. Viral shedding increased sharply between 0.5 and 1 day after challenge and consistently peaked on day 2. The duration of viral shedding averaged over 375 participants was 4.80 days (95% confidence interval: 4.31, 5.29). The frequency of symptomatic infection was 66.9% (95% confidence interval: 58.3, 74.5). Fever was observed in 37.0% of A/H1N1, 40.6% of A/H3N2 (p = 0.86), and 7.5% of B infections (p = 0.001). The total symptoms scores increased on day 1 and peaked on day 3. Systemic symptoms peaked on day 2. No such data exist for children or elderly subjects, but epidemiologic studies suggest that the natural history might differ. The present analysis confirms prior expert opinion on the duration of viral shedding or the frequency of asymptomatic influenza infection, extends prior knowledge on the dynamics of viral shedding and symptoms, and provides original results on the frequency of respiratory symptoms or fever.

965Β citations


"Multi-route respiratory infection: ..." refers background in this paper

  • ...Human challenge models are expensive and often unethical (17) ....

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Ignatius Tak-sun Yu1, Yuguo Li2, Tze Wai Wong1, Wilson W.S. Tam3Β  +4 moreβ€’Institutions (3)
TL;DR: Airborne spread of the virus appears to explain this large community outbreak of SARS in Hong Kong, and future efforts at prevention and control must take into consideration the potential for airborne spread of this virus.
Abstract: background There is uncertainty about the mode of transmission of the severe acute respiratory syndrome (SARS) virus. We analyzed the temporal and spatial distributions of cases in a large community outbreak of SARS in Hong Kong and examined the correlation of these data with the three-dimensional spread of a virus-laden aerosol plume that was modeled using studies of airflow dynamics. methods We determined the distribution of the initial 187 cases of SARS in the Amoy Gardens housing complex in 2003 according to the date of onset and location of residence. We then studied the association between the location (building, floor, and direction the apartment unit faced) and the probability of infection using logistic regression. The spread of the airborne, virus-laden aerosols generated by the index patient was modeled with the use of airflow-dynamics studies, including studies performed with the use of computational fluid-dynamics and multizone modeling. results The curves of the epidemic suggested a common source of the outbreak. All but 5 patients lived in seven buildings (A to G), and the index patient and more than half the other patients with SARS (99 patients) lived in building E. Residents of the floors at the middle and upper levels in building E were at a significantly higher risk than residents on lower floors; this finding is consistent with a rising plume of contaminated warm air in the air shaft generated from a middle-level apartment unit. The risks for the different units matched the virus concentrations predicted with the use of multizone modeling. The distribution of risk in buildings B, C, and D corresponded well with the three-dimensional spread of virus-laden aerosols predicted with the use of computational fluiddynamics modeling. conclusions Airborne spread of the virus appears to explain this large community outbreak of SARS, and future efforts at prevention and control must take into consideration the potential for airborne spread of this virus.

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"Multi-route respiratory infection: ..." refers methods in this paper

  • ...Dsa k = βˆ‘ Dsa k,j Nk j=1 = βˆ‘ πΔti,j k R0 6 si,jtg( Ξ± 2 ) ∫ G(d0)E(dr)d0 (3)L(j, T)ddr da 0 Nk j=1 [3]...

    [...]

  • ...Similar to Equation [3], we have: 𝐷𝑖𝑛 π‘˜ = βˆ‘ πœ‹π›₯𝑑𝑖,𝑗 π‘˜ 𝑅0 6 𝑠𝑖,𝑗𝑑𝑔( 𝛼 2 ) ∫ 𝑃(𝑑0, 𝑠𝑖,𝑗 ,U)𝐺(𝑑0)𝐸(𝑑𝑏)𝑑0 3𝐿(𝑗, 𝑇)πœ‰ 𝑑𝑑 (π‘‘π‘œ)𝑑𝑑𝑏 𝑑𝑏,max π‘‘π‘Ž π‘π‘˜ 𝑗=1 [4] Different from Equation [3], Equation [4] has two additional parameters: πœ‰π‘‘π‘‘ (π‘‘π‘œ)- virus concentration dilution factor in larger droplets; and 𝑃(𝑑0, 𝑠𝑖,𝑗 ,U) - probability of droplets with an initial size of 𝑑0 to reach distance 𝑠𝑖,𝑗 before falling out of the respiratory jet at an initial speed (π‘ˆ)....

    [...]

  • ...Different from Equation [3], Equation [4] has two additional parameters: ΞΎdd (do)virus concentration dilution factor in larger droplets; and P(d0, si,j ,U) - probability of droplets with an initial size of d0 to reach distance si,j before falling out of the respiratory jet at an initial speed (U)....

    [...]


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Alimuddin Zumla1, David S.C. Hui2, Stanley Perlman3β€’Institutions (3)
Abstract: The Middle East respiratory syndrome coronavirus (MERS-CoV) is a lethal zoonotic pathogen that was first identified in humans in Saudi Arabia and Jordan in 2012. Intermittent sporadic cases, community clusters, and nosocomial outbreaks of MERS-CoV continue to occur. Between April 2012 and December 2019, 2499 laboratory-confirmed cases of MERS-CoV infection, including 858 deaths (34Β·3% mortality) were reported from 27 countries to WHO, the majority of which were reported by Saudi Arabia (2106 cases, 780 deaths). Large outbreaks of human-to-human transmission have occurred, the largest in Riyadh and Jeddah in 2014 and in South Korea in 2015. MERS-CoV remains a high-threat pathogen identified by WHO as a priority pathogen because it causes severe disease that has a high mortality rate, epidemic potential, and no medical countermeasures. This Seminar provides an update on the current knowledge and perspectives on MERS epidemiology, virology, mode of transmission, pathogenesis, diagnosis, clinical features, management, infection control, development of new therapeutics and vaccines, and highlights unanswered questions and priorities for research, improved management, and prevention.

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