Secondary Infections

Details of the epidemic, including clinical and laboratory findings for all patients, will be reported elsewhere (M.R. Duffy et al., unpub. data). A subset of ZIKV-infected patients for whom acute- and convalescent-phase paired serum specimens had been collected was analyzed by using several serologic assays to evaluate the extent of cross-reactivity to several related flaviviruses. Patients were classified as primary flavivirus/ZIKV infected or secondary flavivirus/ZIKV probable infected. Primary flavivirus/ZIKV–infected patients were those in whom acute-phase serum specimens (<10 days) had no detectible antibodies (by IgG ELISA and plaque-reduction neutralization test [PRNT]) to any of the heterologous flaviviruses tested (Tables 1, 2) and were either IgM-positive in their acute-phase specimen or IgM and IgG positive for ZIKV in a convalescent-phase specimen (seroconversion). Secondary flavivirus/ZIKV probable–infected patients were those who had detectable antibodies to >1 heterologous flaviviruses in their acute-phase specimen and were also IgM positive for ZIKV in their acute-phase specimen, or IgM and IgG positive for ZIKV in their convalescent-phase specimen. The designation “ZIKV probable” was used because secondary flavivirus infections demonstrate extensive cross-reactivity with other flaviviruses, and in some cases, higher serologic reactivity to the original infecting flavivirus (“original antigenic sin” phenomenon). Thus, in secondary flavivirus infections shown in Tables 1 and 2, serologic data alone is insufficient to confirm ZIKV as the recently infecting flavivirus. However, these secondary flavivirus/ZIKV probable infections were likely recent ZIKV infections because ZIKV was the only virus detected during the epidemic in Yap, a relatively small and isolated island (11 ).

In the model, we divided individuals into four infection classes, as follows: susceptible, exposed (but not yet infectious), infectious, and removed (ie, isolated, recovered, or otherwise no longer infectious; figure 1). The model accounted for delays in symptom onset and reporting by including compartments to reflect transitions between reporting states and disease states. The model also incorporated uncertainty in case observation, by explicitly modelling a Poisson observed process of newly symptomatic cases, reported onsets of new cases, reported confirmation of cases, and a binomial observation process for infection prevalence on evacuation flights (appendix pp 1–3). The incubation period was assumed to be Erlang distributed with mean 5·2 days¹⁴ (link) (SD 3·7) and delay from onset to isolation was assumed to be Erlang distributed with mean 2·9 days (2·1).2 , 15 (link) The delay from onset to reporting was assumed to be exponentially distributed with mean 6·1 days (2·5).² Once exposed to infection, a proportion of individuals travelled internationally and we assumed that the probability of cases being exported from Wuhan to a specific other country depended on the number of cases in Wuhan, the number of outbound travellers (assumed to be 3300 per day before travel restrictions were introduced on Jan 23, 2020, and zero after), the relative connectivity of different countries,¹⁶ and the relative probability of reporting a case outside Wuhan, to account for differences in clinical case definition, detection, and reporting within Wuhan and internationally. We considered the 20 countries outside China most at risk of exported cases in the analysis.Figure 1

Model structure

The population is divided into the following four classes: susceptible, exposed (and not yet symptomatic), infectious (and symptomatic), and removed (ie, isolated, recovered, or otherwise non-infectious). A fraction of exposed individuals subsequently travel and are eventually detected in their destination country.

We modelled transmission as a geometric random walk process, and we used sequential Monte Carlo simulation to infer the transmission rate over time, as well as the resulting number of cases and the time-varying basic reproduction number (R_t), defined here as the mean number of secondary cases generated by a typical infectious individual on each day in a full susceptible population. The model had three unknown parameters, which we estimated: magnitude of temporal variability in transmission, proportion of cases that would eventually be detectable, and relative probability of reporting a confirmed case within Wuhan compared with an internationally exported case that originated in Wuhan. We assumed the outbreak started with a single infectious case on Nov 22, 2019, and the entire population was initially susceptible. Once we had estimated R_t, we used a branching process with a negative binomial offspring distribution to calculate the probability an introduced case would cause a large outbreak. We also did a sensitivity analysis on the following three key assumptions: we assumed the initial number of cases was ten rather than one; we assumed connectivity between countries followed WorldPop rather than MOBS Lab estimates; and we assumed that cases were infectious during the second half of their incubation period rather than only being infectious while symptomatic. All data and code required to reproduce the analysis is available online.

This was a retrospective study approved by the Institutional Review Board of The First Affiliated Hospital of Soochow University, and informed consent was obtained from all patients.
Between January 2019 to December 2020, 223 patients (185 females and 38 males) who were diagnosed as single level OVF and treated by PKP surgery in our institution were included in this study. The included patients were classified into two groups according to the presence or absence of fascia injury (With-FI group and Without-FI group). The average age of the patients was 70.35 ± 8.34 years (ranging from 55 to 93 years).
Inclusion criteria: 1) bone marrow edema in the affected vertebra with or without posterior fascia injury in MR images; 2) severe back pain unresponsive to conservative treatment for at least one week; 3) T-scores assessed by Dual Energy X-ray Absorptiometry was less than -2.5.
Exclusion criteria: 1) pathological fracture secondary to infection or malignancy; 2) unable to tolerate the operation; 3) severe OVF presented fracture dislocation; 4) neurological deficit caused by OVF; 5) combined with Alzheimer's disease or other diseases which made the patients unable to finish information collection on their own.
The time interval between fracture and PKP surgery was recorded as fracture age. The OVF related trauma history was evaluated and the severity of trauma was divided into three degrees (none or minor, moderate, and severe). Vertebral fractures caused by coughing, twisting waist, and other daily activities were considered minor trauma. Moderate trauma was defined as stumble, falls from chairs, and other minor accidents caused by definite external forces. Serious trauma was referred to car accidents, falls from high places and other damages caused by severe external forces.