After a major outbreak of the coronavirus disease (COVID-19) starting in late December 2019, there were no new cases reported in mainland China for the first time on March 18, 2020, and no new cases reported in Hong Kong Special Administrative Region on April 20, 2020. However, these places had reported new cases and experienced a second wave since June 11, 2020. Here we develop a stochastic discrete-time epidemic model to evaluate the risk of COVID-19 resurgence by analyzing the data from the beginning of the outbreak to the second wave in these three places. In the model, we use an input parameter to represent a few potential risks that may cause a second wave, including asymptomatic infection, imported cases from other places, and virus from the environment such as frozen food packages. The effect of physical distancing restrictions imposed at different stages of the outbreak is also included in the model. Model simulations show that the magnitude of the input and the time between the initial entry and subsequent case confirmation significantly affect the probability of the second wave occurrence. Although the susceptible population size does not change the probability of resurgence, it can influence the severity of the outbreak when a second wave occurs. Therefore, to prevent the occurrence of a future wave, timely screening and detection are needed to identify infected cases in the early stage of infection. When infected cases appear, various measures such as contact tracing and quarantine should be followed to reduce the size of susceptible population in order to mitigate the COVID-19 outbreak.
Citation: Sha He, Jie Yang, Mengqi He, Dingding Yan, Sanyi Tang, Libin Rong. The risk of future waves of COVID-19: modeling and data analysis[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5409-5426. doi: 10.3934/mbe.2021274
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After a major outbreak of the coronavirus disease (COVID-19) starting in late December 2019, there were no new cases reported in mainland China for the first time on March 18, 2020, and no new cases reported in Hong Kong Special Administrative Region on April 20, 2020. However, these places had reported new cases and experienced a second wave since June 11, 2020. Here we develop a stochastic discrete-time epidemic model to evaluate the risk of COVID-19 resurgence by analyzing the data from the beginning of the outbreak to the second wave in these three places. In the model, we use an input parameter to represent a few potential risks that may cause a second wave, including asymptomatic infection, imported cases from other places, and virus from the environment such as frozen food packages. The effect of physical distancing restrictions imposed at different stages of the outbreak is also included in the model. Model simulations show that the magnitude of the input and the time between the initial entry and subsequent case confirmation significantly affect the probability of the second wave occurrence. Although the susceptible population size does not change the probability of resurgence, it can influence the severity of the outbreak when a second wave occurs. Therefore, to prevent the occurrence of a future wave, timely screening and detection are needed to identify infected cases in the early stage of infection. When infected cases appear, various measures such as contact tracing and quarantine should be followed to reduce the size of susceptible population in order to mitigate the COVID-19 outbreak.
Let
Let
Let
Theorem 1.1. (see [4,Theorem 4.1]) Let
(1) For any
(2) For any
(3)
Moreover,
sup{G(P)−dimM∣M∈ModR}=sup{G(I)−codimM∣M∈ModR}. |
In this case, we say that
The main goal of this paper is to generalize Theorem 1.1 to Gorenstein subcategories
Theorem 1.2. Let
(1) For any object
(2) For any object
(3)
Moreover,
sup{G(X)−dimM∣M∈A}=sup{G(Y)−codimM∣M∈A}. |
The common value of the last equality is called the Gorenstein global dimension of the abelian category
The proof of the above results will be carried out in the next section.
Throughout this section, we always assume that
Definition 2.1. (see [3,Definition 1.1]) A pair
(BP0) the subcategory
(BP1) for each object
(BP2) for each object
We say that a contravariantly finite subcategory
Lemma 2.2. If the short exact sequence
(1) If
(2) If
(3) If
(4) If
Proof. We just prove (1) and (2) since (3) and (4) follow by duality.
(1) The first statement follows from [1,Proposition 2.13(1)]. One can prove that
(2) The "only if" part is clear. For the "if" part, since
sup{G(X)−dimM∣M∈A}=sup{G(Y)−codimM∣M∈A}. |
By [3,Propsotion 2.2], all rows and columns are
Recall that the
sup{G(X)−dimM∣M∈A}=sup{G(Y)−codimM∣M∈A}. |
If there is no such an integer, set
Lemma 2.3. The following are true for any object
(1) If
(2) If
Proof. We just prove (1) since (2) follows by duality. If
0→Pn→Pn−1→⋯→P1→P0→A→0 |
with all
0→K−j→Q−j−1→K−j−1→0 |
where
We let
Proposition 2.4. The following are true for any
(1) If
In this case,
(2) If
In this case,
Proof. We just prove (1) since (2) follows by duality.
Proposition 2.5. The following are true for any
(1) If
0→K→G→A→0 and 0→A→L→G′→0 |
such that
(2) If
0→A→G→K→0 and 0→G′→L→A→0 |
such that
Proof. According to Lemma 2.2(1), the results follows by an argument similar to that of Proposition 3.3 in [17].
Corollary 2.6. The following are true for any object
(1) If
(2) If
Proof. We just prove (1) since (2) follows by duality. It is clear
0→A→G→K→0 and 0→G′→L→A→0 |
where all rows and columns are
Proposition 2.7. If
sup{Y−cores.dimP∣P∈X}=sup{X−res.dimI∣I∈Y}⩽n. |
Proof. Suppose
Let
Proof of Theorem 1.2.
Let
0→A→I0→I1→⋯ |
is a
0→Ki→Ii→Ki+1→0,i⩾0. |
Here
0→Ki→Ii→Ki+1→0,i⩾0. |
with
Ωn(Ki)→Ωn(Ii)→Ωn(Ki+1),i⩾0, |
together, there is a
0→Ωn(A)→Ωn(I0)→Ωn(I1)→⋯ |
with
⋯→Ωn(P1)→Ωn(P0)→Ωn(A)→0 |
with
⋯→Ωn(P1)→Ωn(P0)→Ωn(I0)→Ωn(I1)→⋯ |
with each term in
Dually, one can prove
The last equality is immediate from above equivalences and Proposition 2.7.
Let
Corollary 2.8. Let
sup{pidRP∣P∈PP(R)}=sup{ppdRI∣I∈PI(R)}⩽n. |
Let
Corollary 2.9. Let
sup{G(I)−codimP∣P∈G(P)}=sup{G(P)−dimI∣I∈G(I)}⩽n. |
Let
Corollary 2.10. Let
(1) For any finitely generated left
(2) For any finitely generated left
(3)
Moreover,
sup{G(P(F))−dimM∣M∈modΛ}=sup{G(I(F))−codimM∣M∈modΛ}. |
The authors thank the referees for their careful reading and excellent suggestions.
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