Phase-shifting of the transmissibility of macrolide-sensitive and resistant Mycoplasma pneumoniae epidemics in Hong Kong, from 2015 to 2018

Antimicrobial Susceptibility Profiles and Genetic Characteristics of Mycoplasma pneumoniae in Shanghai, China, from 2017 to 2019

Objective

The current study investigated the recent genetic characteristics and antimicrobial susceptibility profiles of Mycoplasma pneumoniae (M. pneumoniae) in Shanghai, becoming a clinical reference for treating M. pneumoniae infection in Shanghai.

Methods

Clinical strains were isolated from nasopharyngeal aspirates of the pediatric patients in Shanghai from 2017 to 2019. Nine antimicrobial agents of three antimicrobial classes macrolides, fluoroquinolones and tetracyclines, against M. pneumoniae isolates were investigated using the broth microdilution method. The mechanism of macrolide resistance was analyzed by evaluating the sequences of the 23S rRNA gene and the ribosomal protein genes L4 and L22. Molecular genotyping was undergone to classify the P1 subtypes and the multi-locus variable-number tandem-repeat analysis (MLVA) types.

Results

A total of 72 isolates were resistant to macrolides (MICs > 64 mg/L for erythromycin) based on the A2063G mutation in the 23S rRNA gene. These strains were susceptible to tetracyclines and fluoroquinolones. P1 type 1 (166/182, 91.2%) and MLVA type 4-5-7-2 (165/182, 90.7%) were the dominant subtypes. MLVA type was associated with the P1 subtypes. The distribution of the P1 subtypes and MLVA types did not change over time. The macrolide-resistant rate in P1 type 2 and MLVA type 3-5-6-2 strains were increased during the three-year study. The 5-loci MLVA typing scheme revealed the clonal expansion of MLVA type 3-4-5-7-2 strains which are macrolide-resistant in 2019.

Conclusion

Macrolide resistance in M. pneumoniae in Shanghai is very high and is evolving among certain subtypes. Cautions should be taken for the possible clonal spreading of macrolide-resistant genotypes within this populated region.

Coherence of dengue incidence and climate in the wet and dry zones of Sri Lanka

We studied the dynamics of dengue disease in two epidemic regions in Sri Lanka, the densely populated Colombo district representing the wet zone and the relatively less populated Batticaloa district representing the dry zone. Regional differences in disease dynamics were analysed against regional weather factors. Wavelets, Granger causality and regression methods were used. The difference between the dynamical features of these two regions may be explained by the differences in the climatic characteristics of the two regions. Wavelet analysis revealed that Colombo dengue incidence has 6 months periodicity while Batticaloa dengue incidence has 1 year periodicity. This is well explained by the dominant 6 months periodicity in Colombo rainfall and 1 year periodicity in Batticaloa rainfall. The association between dengue incidence and temperature was negative in dry Batticaloa and was insignificant in wet Colombo. Granger causality results indicated that rainfall, rainy days, relative humidity and wind speed can be used to predict Colombo dengue incidence while only rainfall and relative humidity were significant in Batticaloa. Negative binomial and linear regression models were used to identify the weather variables which best explain the variations in dengue incidence. Most recent available incidence data performed as best explanatory variables, outweighing the importance of past weather data. Therefore we recommend the health authorities to closely monitor the number of cases and to streamline recording procedures so that most recent data are available for early detection of epidemics. We also noted that epidemic responses to weather changes appear quickly in densely populated Colombo compared to less populated Batticaloa. The past dengue incidence and weather variables explain the dengue incidence better in Batticaloa than in Colombo and thus other exogenous factors such as population density and human mobility may be affecting Colombo dengue incidence.

Modelling the Measles Outbreak at Hong Kong International Airport in 2019: A Data-Driven Analysis on the Effects of Timely Reporting and Public Awareness

Background

Measles, a highly contagious disease, still poses a huge burden worldwide. Lately, a trend of resurgence threatened the developed countries. A measles outbreak occurred in the Hong Kong International Airport (HKIA) between March and April 2019, which infected 29 airport staff. During the outbreak, multiple measures were taken including daily situation updates, setting up a public enquiry platform on March 23, and an emergent vaccination program targeting unprotected staff. The outbreak was put out promptly. The effectiveness of these measures was unclear.

Methods

We quantified the transmissibility of outbreak in HKIA by the effective reproduction number, R_eff(t), and basic reproduction number, R₀(t). The reproduction number was modelled as a function of its determinants that were statistically examined, including lags in hospitalization, situation update, and level of public awareness. Then, we considered a hypothetical no-measure scenario when improvements in reporting and public enquiry were absent and calculated the number of infected airport staff.

Results

Our estimated average R₀ is 10.09 (95% CI: 1.73−36.50). We found that R₀(t) was positively associated with lags in hospitalization and situation update, while negatively associated with the level of public awareness. The average predicted basic reproduction number, r₀, was 14.67 (95% CI: 9.01−45.32) under the no-measure scenario, which increased the average R₀ by 77.57% (95% CI: 1.71−111.15). The total number of infected staff would be 179 (IQR: 90−339, 95% CI: 23−821), namely the measure induced 8.42-fold (95% CI: 0.21−42.21) reduction in the total number of infected staff.

Conclusion

Timely reporting on outbreak situation and public awareness measured by the number of public enquiries helped to control the outbreak.

Modelling the effective reproduction number of vector-borne diseases: the yellow fever outbreak in Luanda, Angola 2015-2016 as an example

The burden of vector-borne diseases (Dengue, Zika virus, yellow fever, etc.) gradually increased in the past decade across the globe. Mathematical modelling on infectious diseases helps to study the transmission dynamics of the pathogens. Theoretically, the diseases can be controlled and eventually eradicated by maintaining the effective reproduction number, (R eff ), strictly less than 1. We established a vector-host compartmental model, and derived (R eff ) for vector-borne diseases. The analytic form of the (R eff ) was found to be the product of the basic reproduction number and the geometric average of the susceptibilities of the host and vector populations. The (R eff ) formula was demonstrated to be consistent with the estimates of the 2015-2016 yellow fever outbreak in Luanda, and distinguished the second minor epidemic wave. For those using the compartmental model to study the vector-borne infectious disease epidemics, we further remark that it is important to be aware of whether one or two generations is considered for the transition "from host to vector to host" in reproduction number calculation.

Public awareness, news promptness and the measles outbreak in Hong Kong from March to April, 2019

Background: Globally, a resurgence of measles during the last decade may be attributed to many factors. An unexpected measles outbreak occurred in Hong Kong, and infected 29 airport staff between March and April 2019. The authority updated public on new cases daily, a public enquiry telephone/online platform was set up on March 23, and an emergent vaccination programme was launched targeting unvaccinated airport staff. We aimed to study this measles outbreak and its related factors.Methods: We quantified the transmissibility of the outbreak by the time-varying effective reproduction number, R_eff(t), and inferred the time-varying basic reproduction number, R₀(t). We examined the statistical associations between local public awareness or reporting delay and the R₀(t).Results: Our estimated average R₀ is 10.7 with 95% CI of 6.0-29.2. We found that R₀(t) was negatively associated with the level of public awareness and the level of promptness of situation updates on new cases.Conclusion: Public awareness via situation updates helped to control the outbreak. The medical effects of the vaccination programme was not soon enough to cause the immediate shutting down of the outbreak, but it boosted herd immunity to prevent future airport outbreaks in the next few years.

Large-scale Lassa fever outbreaks in Nigeria: quantifying the association between disease reproduction number and local rainfall

Lassa fever (LF) is increasingly recognised as an important rodent-borne viral haemorrhagic fever presenting a severe public health threat to sub-Saharan West Africa. In 2017–18, LF caused an unprecedented epidemic in Nigeria and the situation was worsening in 2018–19. This work aims to study the epidemiological features of epidemics in different Nigerian regions and quantify the association between reproduction number (R) and state rainfall. We quantify the infectivity of LF by the reproduction numbers estimated from four different growth models: the Richards, three-parameter logistic, Gompertz and Weibull growth models. LF surveillance data are used to fit the growth models and estimate the Rs and epidemic turning points (τ) in different regions at different time periods. Cochran's Q test is further applied to test the spatial heterogeneity of the LF epidemics. A linear random-effect regression model is adopted to quantify the association between R and state rainfall with various lag terms. Our estimated Rs for 2017–18 (1.33 with 95% CI 1.29–1.37) was significantly higher than those for 2016–17 (1.23 with 95% CI: (1.22, 1.24)) and 2018–19 (ranged from 1.08 to 1.36). We report spatial heterogeneity in the Rs for epidemics in different Nigerian regions. We find that a one-unit (mm) increase in average monthly rainfall over the past 7 months could cause a 0.62% (95% CI 0.20%–1.05%)) rise in R. There is significant spatial heterogeneity in the LF epidemics in different Nigerian regions. We report clear evidence of rainfall impacts on LF epidemics in Nigeria and quantify the impact.