At the end of 2022, a total of 20,003 diagnoses of human immunodeficiency virus (HIV) infection and 8,983 cases of acquired immunodeficiency syndrome (AIDS) among Japanese nationals, and 3,860 HIV diagnoses and 1,575 AIDS cases among foreign residents, had been notified to the government in Japan. This study updates the estimate of HIV incidence, including during the COVID-19 pandemic. It aimed to reconstruct the incidence of HIV and understand how the disruption caused by COVID-19 affected the epidemiology of HIV. Using a median incubation period of 10.0 years, the number of undiagnosed HIV infections was estimated to be 3,209 (95% confidence interval (CI): 2,642, 3,710) at the end of 2022. This figure has declined steadily over the past 10 years. Assuming that the median incubation period was 10.0 years, the proportion of diagnosed HIV infections, including surviving AIDS cases, was 89.3% (95% CI: 87.8%, 91.0%). When AIDS cases were excluded, the proportion was 86.2% (95% CI: 84.3%, 88.3%). During the COVID-19 pandemic, the estimated annual diagnosis rate was slightly lower than during earlier time intervals, at around 16.5% (95% CI: 14.9%, 18.1%). Japan may already have achieved diagnostic coverage of 90%, given its 9% increment in the diagnosed proportion during the past 5 years. The incidence of HIV infection continued to decrease even during the COVID-19 pandemic from 2020 to 2022, and the annual rate of diagnosis decreased slightly to 16.5%. Monitoring the recovery of diagnosis along with the effective reproduction number is vital in the future.
Citation: Hiroshi Nishiura, Seiko Fujiwara, Akifumi Imamura, Takuma Shirasaka. HIV incidence before and during the COVID-19 pandemic in Japan[J]. Mathematical Biosciences and Engineering, 2024, 21(4): 4874-4885. doi: 10.3934/mbe.2024215
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Abstract
At the end of 2022, a total of 20,003 diagnoses of human immunodeficiency virus (HIV) infection and 8,983 cases of acquired immunodeficiency syndrome (AIDS) among Japanese nationals, and 3,860 HIV diagnoses and 1,575 AIDS cases among foreign residents, had been notified to the government in Japan. This study updates the estimate of HIV incidence, including during the COVID-19 pandemic. It aimed to reconstruct the incidence of HIV and understand how the disruption caused by COVID-19 affected the epidemiology of HIV. Using a median incubation period of 10.0 years, the number of undiagnosed HIV infections was estimated to be 3,209 (95% confidence interval (CI): 2,642, 3,710) at the end of 2022. This figure has declined steadily over the past 10 years. Assuming that the median incubation period was 10.0 years, the proportion of diagnosed HIV infections, including surviving AIDS cases, was 89.3% (95% CI: 87.8%, 91.0%). When AIDS cases were excluded, the proportion was 86.2% (95% CI: 84.3%, 88.3%). During the COVID-19 pandemic, the estimated annual diagnosis rate was slightly lower than during earlier time intervals, at around 16.5% (95% CI: 14.9%, 18.1%). Japan may already have achieved diagnostic coverage of 90%, given its 9% increment in the diagnosed proportion during the past 5 years. The incidence of HIV infection continued to decrease even during the COVID-19 pandemic from 2020 to 2022, and the annual rate of diagnosis decreased slightly to 16.5%. Monitoring the recovery of diagnosis along with the effective reproduction number is vital in the future.
1.
Introduction
The linear complexity and the k-error linear complexity are important cryptographic characteristics of stream cipher sequences. The linear complexity of an N-periodic sequence s={su}∞u=0, denoted by LC(s), is defined as the length of the shortest linear feedback shift register (LFSR) that generates it [1]. With the Berlekamp-Massey (B-M) algorithm [2], if LC(s)≥N/2, then s is regarded as a good sequence with respect to its linear complexity. For an integer k≥0, the k-error linear complexity LCk(s) is the smallest linear complexity that can be obtained by changing at most k terms of s in the first period and periodically continued [3]. The cryptographic background of the k-error linear complexity is that some key streams with large linear complexity can be approximated by some sequences with much lower linear complexity [2]. For a sequence to be cryptographically strong, its linear complexity should be large enough, and its k-error linear complexity should be close to the linear complexity.
The relationship between the linear complexity and the DFT of the sequence was given by Blahut in [4]. Let m be the order of 2 modulo an odd number N. For a primitive N-th root β∈F2m of unity, the DFT of s is defined by
ρi=N−1∑u=0suβ−iu, 0≤i≤N−1.
(1.1)
Then
LC(s)=WH(ρ0,ρ1,⋯,ρN−1),
(1.2)
where WH(A) is the hamming weight of the sequence A. The polynomial
G(X)=N−1∑i=0ρiXi∈F2m[X]
(1.3)
is called the Mattson-Solomon polynomial (M-S polynomial) of s[5]. It can be deduced from Eqs (1.2)and (1.3) that the linear complexity of s is equal to the number of the nonzero terms of G(X), namely
LC(s)=|G(X)|.
(1.4)
By the inverse DFT,
su=N−1∑i=0ρiβiu=G(βu), 0≤u≤N−1.
(1.5)
There are many studies about two-prime generators. In 1997–1998, Ding calculated the linear complexity and the autocorrelation values of binary Whiteman generalized cyclotomic sequences of order two [6,7]. In 2013, Li defined a new generalized cyclotomic sequence of order two of length pq, which is based on Whiteman generalized cyclotomic classes, and calculated its linear complexity [8]. In 2015, Wei determined the k-error linear complexity of Legendre sequences for k=1,2[9]. In 2018, Hofer and Winterhof studied the 2-adic complexity of the two-prime generator of period pq[10]. Alecu and Sălăgean transformed the optimisation problem of finding the k-error linear complexity of a sequence into an optimisation problem in the DFT domain, by using Blahut's theorem in the same year [11]. In 2019, in terms of the DFT, Chen and Wu discussed the k-error linear complexity for Legendre, Ding-Helleseth-Lam, and Hall's sextic residue sequences of odd prime period p[12]. In 2020, Zhou and Liu presented a type of binary sequences based on a general two-prime generalized cyclotomy, and derived their minimal polynomial and linear complexity [13]. In 2021, the autocorrelation distribution and the 2-adic complexity of generalized cyclotomic binary sequences of order 2 with period pq were determined by Jing [14].
This paper is organized as follows. Firstly, we present some preliminaries about Whiteman generalized cyclotomic classes and the linear complexity in Section 2. In Section 3, we give main results about the linear complexity of Whiteman generalized cyclotomic sequences of order two. In Section 4, we give the 1-error linear complexity of these sequences. At last, we conclude this paper in Section 5.
2.
Preliminaries
Let p and q be two distinct odd primes with gcd(p−1,q−1)=2, and N=pq, e=(p−1)(q−1)/2. By the Chinese Remainder Theorem, there is a fixed common primitive root g of both p and q such that ordN(g)=e. Let x be an integer satisfying
x=g(modp), x=1(modq).
Then the set
Di={gsximodN:s=0,1,⋯,e−1}
for i=0,1 is called a Whiteman generalized cyclotomic class of order two [15].
As pointed out in [14], the unit group of the ring ZN is
Actually, if p≡−1(mod8) or p≡3(mod8), then (p−1)/2=1; if p≡1(mod8) or p≡−3(mod8), then (p−1)/2=0. By symmetry, if q≡−1(mod8) or q≡3(mod8), then (q−1)/2=1; if q≡1(mod8) or q≡−3(mod8), then (q−1)/2=0.
Lemma 2.5.Define
Di(X)=∑u∈DiXu∈F2[X],i=0,1.
Then for β, we have D0(β)=0 and D1(β)=1 if 2∈D0; D0(β)=ω and D1(β)=1+ω if 2∈D1, where ω∈F4∖F2.
And by Eq (2.1), we have D0(β)≠D1(β) and D0(β)+D1(β)=1. Assume that D0(β)=0, D1(β)=1 for 2∈D0, and D0(β)=ω, D1(β)=1+ω for 2∈D1, where ω∈F4∖F2.
3.
The linear complexity of s(a,b,c)
Let LC(s(a,b,c)) be the linear complexity of s(a,b,c), and the other symbols be the same as before.
Theorem 3.1.Let p≡v(mod8) and q≡w(mod8), where v,w=±1,±3. Then the linear complexity of s(a,b,c) respect to different values of p and q is as shown as Table 1.
By Eq (1.4) we can get the linear complexity of s(0,0,0) as
LC(s(0,0,0))=|G(X)|=pq−p.
Actually, the linear complexity of s(1,0,0) was studied by Ding in [6] with its minimal polynomial.
4.
The 1-error linear complexity of s(a,b,c)
Let LCk(s(a,b,c)) be the k-error linear complexity of s(a,b,c), ˜s={˜su}∞u=0 be the new sequence obtained by changing at most k terms of s, that ˜s=s+e, where e={eu}∞u=0 is an error sequence of period N. Ding has provided in [2] that, the k-error linear complexity of a sequence can be expressed as
LCk(s)=minWH(e)≤k{LC(s+e)}.
(4.1)
It is clearly that LC0(s)=LC(s) and
N≥LC0(s)≥LC1(s)≥⋯≥LCl(s)=0,
where l=WH(s).
Let G(X), Gk(X) and ˜G(X) be the M-S polynomials of s, e and ˜s respectively. Note that
where ρi, ηi and ξi are the DFTs of s, e and ˜s respectively. By Eqs (1.5), (4.1) and (4.2), we have ˜G(X)=G(X)+Gk(X), then
ξi=ρi+ηi.
(4.3)
Assume that eu0=1 for 0≤u0≤N−1 and eu=0 for u≠u0 in the first period of e. Then the DFT of e is
ηi=N−1∑u=0euβ−iu=β−iu0, 0≤i≤N−1.
Specially, if u0=0, then ηi=1 for all 0≤i≤N−1; otherwise, η0=1 and the order of ηi is N for 1≤i≤N−1.
Theorem 4.1.Let p≡v(mod8) and q≡w(mod8), where v,w=±1,±3, and the other symbols be the same as before. Then the 1-error linear complexity of s(a,b,c) is as shown as Table 2.
Table 2.
The 1-error linear complexity of s(a,b,c).
Compare the results of Cases (1)–(3) with LC(s(0,0,0))=pq−p. If p>q, then pq−p<pq−q+1<pq; if p<q, then pq−q+1<pq−p<pq. Hence
LC1(s(0,0,0))={pq−p,if p>q,pq−q+1,if p<q.
Similarly we can prove the other cases for LC1(s(a,b,c)).
All results of LC(s(a,b,c)) and LC1(s(a,b,c)) in Sections 3 and 4 have been tested by MAGMA program.
5.
Conclusions
The purpose of this paper is to determine the linear complexity and the 1-error linear complexity of s(a,b,c). In most of the cases, s(a,b,c) possesses high linear complexity, namely LC(s(a,b,c))>N/2, consequently has decent stability against 1-bit error. Notice that the linear complexity of some of the sequences above is close to N/2. Then the sequences can be selected to construct cyclic codes by their minimal generating polynomials with the method introduced by Ding [16].
Acknowledgments
This work was supported by Fundamental Research Funds for the Central Universities (No. 20CX05012A), the Major Scientific and Technological Projects of CNPC under Grant (No. ZD2019-183-008), the National Natural Science Foundation of China (Nos. 61902429, 11775306) and Shandong Provincial Natural Science Foundation of China (ZR2019MF070).
Conflict of interest
The authors declare that they have no conflicts of interest.
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Figure 1. Yearly HIV diagnoses and AIDS incidence in Japan, 1985–2022. (A) Comparisons between observed and predicted yearly numbers of HIV diagnoses and AIDS incidence among Japanese people. Different median incubation periods (i.e., 7.5, 10.0, and 12.3 years) were assumed, but predicted values mostly overlapped. (B) Comparisons between observed and predicted values by sex. Circles represent the observed number of HIV diagnoses and triangles AIDS cases. Solid marks are men and empty marks women. A common logarithmic scale was used on the vertical axis
Figure 2. Estimated HIV incidence and diagnosis rates per year in Japan, 1985–2022. (A) Estimates of the yearly incidence based on maximum likelihood, assuming median incubation periods of 7.5, 10.0, and 12.3 years. (B) Estimates of the yearly rate of diagnosis, assuming median incubation periods of 7.5, 10.0, and 12.3 years
Figure 3. Undiagnosed cases as a proportion of HIV infections in Japan, 1985–2022. (A) Estimates of undiagnosed HIV infections, assuming a median incubation period of 10.0 years. The 95% confidence intervals were determined through the application of profile likelihood. (B) Maximum likelihood estimates of undiagnosed HIV infections for median incubation periods of 7.5, 10.0, and 12.3 years. (C) Diagnosed cases as a proportion of the cumulative number of HIV infections, with AIDS cases also taken into account. (D) Diagnosed cases as a proportion of the cumulative number of HIV infections, without AIDS cases taken into account, by sex
Figure 4. Estimated undiagnosed HIV infections as a proportion of total diagnoses by the end of 2022. (A) Number of undiagnosed infections. (B) Estimates of the diagnosed proportion of HIV infections assuming different incubation periods. For each median incubation period, the estimate on the left (square) is when AIDS cases were excluded, and on the right (circle) when AIDS cases were included. Whiskers extend to lower and upper 95% confidence intervals