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Published at December 3

Towards the efficacy of federated prediction for epidemics on networks

cs.SI

cs.DC

cs.LG

Released Date: December 3, 2024

Authors: Chengpeng Fu¹, Tong Li², Hao Chen¹, Wen Du¹, Zhidong He¹

Aff.: ¹DS Information Technology Co., Ltd.; ²The First Research Institute of Telecommunications Technology Co., Ltd.

Arxiv: http://arxiv.org/pdf/2412.02161v1

Model	Description	Factors
SIS[2]	The spread where individuals can transition between susceptible (S) and infected (I) states, with infected individuals recovering and becoming susceptible again	$\beta$ - the infection rate $\delta$ - the curing rate
SIR[2]	The spread where individuals transition from susceptible (S) to infected (I) and then to recovered (R), with recovered individuals gaining immunity and no longer participating in the infection dynamics	$\beta$ - the infection rate $\delta$ - the curing rate
SEIR[2]	The spread of a disease where individuals progress through four states: susceptible (S), exposed (E, infected but not yet infectious), infected (I), and recovered (R), with recovered individuals gaining immunity.	$\beta_{1}$ - the infection rate $\beta_{2}$ - the incubation rate $\delta$ - the curing rate
non-Markovian SIS[22]	The non-Markovian SIS epidemic model modifies the standard SIS dynamics by using a Weibull distribution (instead of an exponential distribution) for the transmission time, allowing for more flexible and realistic infection and recovery times.	$\beta_{\lambda}$ -the scale parameter $\beta_{k}$ -the shape parameter $\delta$ -the curing rate
SIRS[2]	The dynamics where individuals transition from susceptible (S) to infected (I), then to recovered (R), but after a period of immunity, they can become susceptible again.	$\beta$ - the infection rate $\delta$ - the curing rate $\omega$ - the immunity loss rate
SIRVS[25]	The SIRVS epidemic model extends the SIR framework by incorporating vaccinated (V) and susceptible (S) states, where individuals can transition from susceptible to vaccinated, potentially reducing infection rates and influencing the disease dynamics.	$\beta$ - the infection rate $\delta$ - the curing rate $\omega$ - the immunity loss rate $v_{1}$ - the vaccination rate $v_{2}$ - the vaccine waning rate
SIS with time-varying rates [26]	The SIS epidemic model with time-dependent rates incorporates a seasonal variation in the infection rate, defined as the infection rate $\beta(t)=a+b\sin(t/c)$ in this paper, where $a,b,c$ are parameters.	$\beta(t)$ - the infection rate $\delta$ - the curing rate