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SARS-CoV-2 coinfections:Implications for the second wave
Lubna Pinky∗,‡, Hana M. Dobrovolny †
∗Department of Pediatrics, University of Tennessee Health Science Center†Department of Physics and Astronomy, Texas Christian University, Fort Worth, TX
Background
• The early part of the SARS-CoV-2 pandemiccoincided with circulation of other respiratoryviruses, such as influenza, and the second waveis expected to coincide with the upcoming in-fluenza season.
• Since SARS-CoV-2 is co-circulating withother respiratory viruses, viral coinfections arelikely to occur. However, in the initial phaseof the pandemic, clinicians noted fewer SARS-CoV-2 coinfections than were expected.
• We use mathematical models to investigatethe mechanism for the lack of coinfections andto investigate what that might mean for a sec-ond wave.
In-host coinfection model
In this model, the viruses interact via competi-tion for the resource of cells.
Target cells
Eclipse cells Infectious cells
Infectious cellsEclipse cells
Target cells :dT
dt= −β1TV1 − β2TV2
Eclipse cells :dEi
dt= βiTVi − kiEi
Infected cells :dIidt
= kiEi − δiIi
Virus :dVidt
= piIi − ciVi.
Viruses
We fit single virus models to parameter-ize the model for influenza (IAV), humanrhinovirus (hRV), respiratory syncytial virus(RSV), parainfluenza virus (PIV), and humanmetapneumovirus (hMPV).
0 10 20 30 40Time (d)
101
102
103
104
105
Vir
al l
oad
(co
pie
s/m
L)
SARS-CoV-2
0 2 4 6 8 10Days post infection (dpi)
10-2
100
102
104
106
108
Vir
al t
iter
(P
FU
/mL
)
Model resultExperimental data
IAV
0 2 4 6 8 10Days post infection (dpi)
10-2
100
102
104
106
Vir
al t
iter
(T
CID
50/m
l)
Model result Experimental data
hRV
0 2 4 6 8 10Days post infection (dpi)
10-2
100
102
104
106
Vir
al t
iter
(P
FU
/mL
)
Model resultExperimental data
RSV
0 2 4 6 8 10Days post infection (dpi)
10-2
100
102
104
106
108
Vir
al t
iter
(T
CID
50
/ml)
Model result Experimental data
PIV
0 2 4 6 8 10Days post infection (dpi)
10-2
100
102
104
Vir
al t
iter
(T
CID
50
/ml)
Model resultExperimental data
hMPV
SARS-CoV-2 coinfections
0 2 4 6 8 10 12 14Time (d)
100
102
104
106
108
Vir
al t
iter
CoV-2 single infection
IAV single infection
CoV-2 coinfectionIAV coinfection
0 2 4 6 8 10 12 14Time (d)
100
102
104
106
108
Vir
al t
iter
CoV-2 single infection
RSV single infection
CoV-2 coinfectionRSV coinfection
0 2 4 6 8 10 12 14Time (d)
100
102
104
106
108
Vir
al t
iter
CoV-2 single infection
hRV single infection
CoV-2 coinfectionhRV coinfection
0 2 4 6 8 10 12 14Time (d)
100
102
104
106
108
Vir
al t
iter
CoV-2 single infection
PIV single infection
CoV-2 coinfectionPIV coinfection
0 2 4 6 8 10 12 14Time (d)
100
102
104
106
108
Vir
al t
iter
CoV-2 single infection
hMPV single infection
CoV-2 coinfectionhMPV coinfection
SARS-CoV-2 infections are suppressed by otherrespiratory viruses.
Role of growth rate
Virus IAV RSV hRVGrowth rate (/d) 11.9 5.41 13.6
Virus PIV hMPV SARSGrowth rate (/d) 3.99 9.07 1.80
Viruses with higher growth rates infect cellsfaster than viruses with lower growth rate, leav-ing the slower virus with no cells to to infect.
Sequential infections
0 3 6 9 12 15 18 21 24 27 30Time (d)
10 1
102
105
108
Vira
l tite
r
IAV SARS-CoV-2
0 3 6 9 12 15 18 21 24 27 30Time (d)
10 1
102
105
108
Vira
l tite
r
RSV SARS-CoV-2
0 3 6 9 12 15 18 21 24 27 30Time (d)
10 1
102
105
108
Vira
l tite
r
HRV SARS-CoV-2
0 3 6 9 12 15 18 21 24 27 30Time (d)
10 1
102
105
108
Vira
l tite
r
PIV SARS-CoV-2
0 3 6 9 12 15 18 21 24 27 30Time (d)
10 1
102
105
108
Vira
l tite
r
HMPV SARS-CoV-2
Even if the second infection is initiated afterSARS-CoV-2, it can still suppress SARS-CoV-2.
Epidemiological model
If SARS-CoV-2 is so easily suppressed by otherrespiratory viruses, what might be the impli-cations for a second wave? We developed anepidemiological model where one virus preventscoinfection with the other.
(Susceptible)
(Infected with Flu) (Infected with CoV-2)
Recovered(from Flu)Susceptible(to CoV-2)
(Exposed to Flu)(Exposed to CoV-2)
(Recovered)
(Recovered from CoV-2Susceptible to Flu)
(Infected with Flu)(Infected with CoV-2)
(Coinfected)
k2
δ1
1k
2δ
k2 1k
δ12δ
3δ
(Exposed to CoV-2)(Exposed to Flu)
(Exposed to both)(Recovered from FluSusceptible to CoV-2)
(I +I + )1 3
(I +I + )1 3
S
E2E1
1I 2I
3I
E3
R
(I +I +E + )2 3 3
1k
1S(2)
2E(1) 1E(2)
S2(1)
I(2)1
I(1)2
I(2)1
I(2)1
(I +I + )1 3I(1)2
(I +I + )1 3 I(1)2ß1/N
ß1/N
(I +I +E + )2 3 3I(2)1 2ß /N
ß1/N
2ß /N ß1/N
I(2)1
Epidemiological parameters
Flu (H1N1) SARS-CoV-2βi, d−1 0.50 0.41ki, d−1 0.25 0.20δi, d−1 0.20 0.10δ3, d−1 0.1 or 0.01
Flu parameters from Gonzalez-Parra et al. (2015) Acta TropicaSARS-CoV-2 parameters from Anderson et al. (2020) The Lancet
Two virus epidemic
90 180 270 360 450Time (days)
02468
101214
Infe
cted
pop
ulat
ion
(log 1
0) FluCoV-2CoinfectedSingle infection: FluSingle infection: CoV-2
90 180 270 360 450Time (days)
02468
101214
Rec
over
ed in
fect
ed (l
og10
)
Recovered from Flu,infected with CoV-2Recovered from CoV-2,infected with Flu
90 180 270 360 450Time (days)
02468
101214
Infe
cted
pop
ulat
ion
(log 1
0) FluCoV-2CoinfectedSingle infection: FluSingle infection: CoV-2
90 180 270 360 450Time (days)
02468
101214
Rec
over
ed in
fect
ed (l
og10
)
Recovered from Flu,infected with CoV-2Recovered from CoV-2,infected with Flu
Influenza’s ability to block SARS-CoV-2 leadsto a delay in the SARS-CoV-2 epidemic.
Hospitalization
We looked at the total number of infected peopleas a proxy for possible hospitalization.
90 180 270 360 450Time (days)
02468
101214
Infe
cted
pop
ulat
ion
(log 1
0)
Absolute recovery rate=0.1 d 1
Total Flu and CoV-2infected with coinfectionTotal Flu and CoV-2infected without coinfection
90 180 270 360 450Time (days)
02468
101214
Infe
cted
pop
ulat
ion
(log 1
0)
Absolute recovery rate=0.01 d 1
Total Flu and CoV-2infected with coinfectionTotal Flu and CoV-2infected without coinfection
We find a double peak in the hospitalizations;the first caused by influenza patients, the sec-ond caused by SARS-CoV-2 patients.
Parameter sensitivity
We used partial rank correlation coefficient todetermine which parameters are most importantfor determining the course of the epidemic.
Total Hospitalization Time Between Peaks
Aside from the hospitalization rates (h1 and h2)total number of hospitalized patients is moststrongly determined by the infection rate of in-fluenza while the timing between peaks is deter-mined by both infection rates and the hospital-ization rate of influenza.
Conclusions
• Within the host, coinfecting viruses com-pete for the resource of cells.
• SARS-CoV-2 appears to have a lowerwithin host growth rate than other respi-ratory viruses and tends to be suppressedduring coinfections.
• At the population level, co-circulation ofinfluenza and SARS-CoV-2 leads to a tem-porary suppression of SARS-CoV-2 infec-tions. Once people have recovered frominfluenza, SARS-CoV-2 reappears.
• This results in a double peak in the num-ber of infected people — the first peakcaused by influenza, the second by SARS-CoV-2.