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Background The influenza virus spreads rapidly around the world in seasonal epidemics, resulting in significant morbidity and mortality. Influenza-related incidence data are limited in many countries in Africa despite established sentinel surveillance. This study aimed to address the information gap by estimating the burden and seasonality of medically attended influenza like illness in Ethiopia. Method Influenza sentinel surveillance data collected from 3 influenza like illness (ILI) and 5 Severe Acute Respiratory Illness (SARI) sites from 2012 to 2017 was used for analysis. Descriptive statistics were applied for simple analysis. The proportion of medically attended influenza positive cases and incidence rate of ILI was determined using total admitted patients and catchment area population. Seasonality was estimated based on weekly trend of ILI and predicted threshold was done by applying the “Moving Epidemic Method (MEM)”. Result A total of 5715 medically attended influenza suspected patients who fulfills ILI and SARI case definition (77% ILI and 23% SARI) was enrolled. Laboratory confirmed influenza virus (influenza positive case) among ILI and SARI suspected case was 25% (1130/4426) and 3% (36/1289). Of which, 65% were influenza type A. The predominantly circulating influenza subtype were seasonal influenza A(H3N2) (n = 455, 60%) and Influenza A(H1N1)pdm09 (n = 293, 38.81%). The estimated mean annual influenza positive case proportion and ILI incidence rate was 160.04 and 52.48 per 100,000 population. The Incidence rate of ILI was higher in the age group of 15–44 years of age [‘Incidence rate (R) = 254.6 per 100,000 population’, 95% CI; 173.65, 335.55] and 5–14 years of age [R = 49.5, CI 95%; 31.47, 130.43]. The seasonality of influenza has two peak seasons; in a period from October–December and from April–June. Conclusion Significant morbidity of influenza like illness was observed with two peak seasons of the year and seasonal influenza A (H3N2) remains the predominantly circulating influenza subtype. Further study need to be considered to identify potential risks and improving the surveillance system to continue early detection and monitoring of circulating influenza virus in the country has paramount importance.

A novel corona virus (2019-nCoV) was identified in Wuhan, China and has been causing an unprecedented outbreak in China. The spread of this novel virus can eventually become an international emergency. During the early outbreak phase in Wuhan, one of the most important public health tasks is to prevent the spread of the virus to other cities. Therefore, full-scale border control measures to prevent the spread of virus have been discussed in many nearby countries. At the same time, lockdown in Wuhan cityu (border control from leaving out) has been imposed. The challenge is that many people have traveled from Wuhan to other cities before the border control. Thus, it is difficult to forecast the number of imported cases at different cities and estimate their risk on outbreak emergence.Here, we have developed a mathematical framework incorporating city-to-city connections to calculate the number of imported cases of the novel virus from an outbreak source, and the cumulative number of secondary cases generated by the imported cases. We used this number to estimate the arrival time of outbreak emergence using air travel frequency data from Wuhan to other cities, collected from the International Air Transport Association database. In addition, a meta-population compartmental model was built based on a classical SIR approach to simulate outbreaks at different cities.We consider the scenarios under three basic reproductive number (R0) settings using the best knowledge of the current findings, from high (2.92), mild (1.68), to a much lower numbers (1.4). The mean arrival time of outbreak spreading has been determined. Under the high R0, the critical time is 17.9 days after December 31, 2019 for outbreak spreading. Under the low R0, the critical time is between day 26.2 to day 35 after December 31, 2019. To make an extra 30 days gain, under the low R0 (1.4), the control measures have to reduce 87% of the connections between the source and target cities. Under the higher R0 (2.92), the effect on reducing the chance of outbreak emergence is generally low until the border control measure was enhanced to reduce more than 95% of the connections.

In recent years, dengue has been rapidly spreading and growing in the tropics and subtropics. Located in southern China, Hong Kong’s subtropical monsoon climate may favour dengue vector populations and increase the chance of disease transmissions during the rainy summer season. An increase in local dengue incidence has been observed in Hong Kong ever since the first case in 2002, with an outbreak reaching historically high case numbers in 2018. However, the effects of seasonal climate variability on recent outbreaks are unknown. As the local cases were found to be spatially clustered, we developed a Poisson generalized linear mixed model using pre-summer monthly total rainfall and mean temperature to predict annual dengue incidence (the majority of local cases occur during or after the summer months), over the period 2002-2018 in three pre-defined areas of Hong Kong. Using leave-one-out cross-validation, 5 out of 6 observations of area-specific outbreaks during the major outbreak years 2002 and 2018 were able to be predicted. 42 out of a total of 51 observations (82.4%) were within the 95% confidence interval of the annual incidence predicted by our model. Our study found that the rainfall before and during the East Asian monsoon (pre-summer) rainy season is negatively correlated with the annual incidence in Hong Kong while the temperature is positively correlated. Hence, as mosquito control measures in Hong Kong are intensified mainly when heavy rainfalls occur during or close to summer, our study suggests that a lower-than-average intensity of pre-summer rainfall should also be taken into account as an indicator of increased dengue risk.

Background Influenza is an acute viral disease of the respiratory tract which is characterized by fever, headache, myalgia, prostration, coryza, sore throat and cough. Globally, an estimated 3 to 5 million cases of severe influenza illness and 291 243–645 832 seasonal influenza-associated respiratory deaths occur annually. Although recent efforts from some African countries to describe burden of influenza disease and seasonality, these data are missing for the vast majority, including Ethiopia. Ethiopia established influenza sentinel surveillance in 2008 aiming to determine influenza strains circulating in the country and know characteristics, trend and burden of influenza viruses. Methods We used influenza data from sentinel surveillance sites and respiratory disease outbreak investigations from 2009 to 2015 for this analysis. We obtained the data by monitoring patients with influenza-like illness (ILI) at three health-centers, severe acute respiratory infection (SARI) at five hospitals and investigating patients during different respiratory infection outbreaks. Throat-swab specimens in viral transport media were transported to the national reference laboratory within 72 h of collection using a cold-chain system. We extracted viral RNA from throat-swabs and subjected to real-time PCR amplification. We further subtyped and characterized Influenza A-positive specimens using CDC real-time reverse transcription PCR protocol. Results A total of 4962 throat-swab samples were collected and 4799 (96.7%) of them were tested. Among them 988 (20.6%) were influenza-positive and of which 349 (35.3%) were seasonal influenza A(H3N2), 321 (32.5%) influenza A(H1N1)pdm2009 and 318 (32.0%) influenza B. Positivity rate was 29.5% in persons 5–14 years followed by 26.4% in 15–44 years, 21.2% in > 44 years and 6.4% in under five children. The highest positivity rate observed in November (37.5%) followed by March (27.6%), December (26.4%), October (24.4%) and January (24.3%) while the lowest positivity rate was in August (7.7%). Conclusion In Ethiopia, seasonal Influenza A(H3N2), Influenza A(H1N1)pdm2009 and Influenza B viruses were circulating during 2009–2015. Positivity rate and number of cases peaked in November and December. Influenza is one of public health problems in Ethiopia and the need to introduce influenza vaccine and antivirus is important to prevent and treat the disease in future.

Background Yellow Fever (YF) is a viral hemorrhagic disease transmitted by aedes mosquito species. Approximately, 200,000 cases and 30,000 deaths occur worldwide every year. In Ethiopia, the last outbreak was reported in 1966 with 2200 cases and 450 deaths. A number of cases with deaths from unknown febrile illness reported from South Ari district starting from November 2012. This investigation was conducted to identify the causative agent, source of the outbreak and recommend appropriate interventions. Methods Medical records were reviewed and Patients and clinicians involved in managing the case were interviewed. Descriptive data analysis was done by time, person and place. Serum samples were collected for serological analysis it was done using Enzyme-linked Immunosorbent Assay for initial screening and confirmatory tests were done using Plaque Reduction and Neutralization Test. Breteau and container indices were used for the entomological investigation to determine the risk of epidemic. Results A total of 141 Suspected YF cases with 43 deaths (CFR = 30.5%) were reported from November 2012 to October 2013 from South Omo Zone. All age groups were affected (mean 27.5, Range 1–75 Years). Of the total cases, 85.1% cases had jaundice and 56.7% cases had fever. Seven of the 21 samples were IgM positive for YF virus. Aedes bromeliae and Aedes aegypti were identified as responsible vectors of YF in affected area. The Breteau indices of Arkisha and Aykamer Kebeles were 44.4% and 33.3%, whereas the container indices were 12.9% and 22.2%, respectively. Conclusion The investigation revealed that YF outbreak was reemerged after 50 years in Ethiopia. Vaccination should be given for the affected and neighboring districts and Case based surveillance should be initiated to detect every case.

Chikungunya is a viral disease (genus Alphavirus) which is transmitted to humans by infected mosquitoes—including Aedes aegypti and A. albopictus. An outbreak of febrile illness, suspected to have been caused by chikungunya, was reported in June 2016 from Dolloado district, Suuf Kebele, in the Somalia regional state of Ethiopia that borders the Mandera county of Kenya where a confirmed chikungunya outbreak was ongoing. Laboratory investigation was carried out to confirm if the outbreak in Ethiopia was caused by Chikungunya virus. Ten serum samples were collected from suspected patients visiting a health center in Suuf Kebel, who were then sent to the Nation laboratory in Ethiopian Public Health Institute. RNA was extracted from the serum samples using QIAgene RNA Mini kit, and PCR detection of dengue, chikungunya, and Zika virus nucleic acid was done using Trioplex Real-time RT-PCR Assay following the protocol from the Center for Disease Control (CDC). The Trioplex Real-time RT-PCR assay, for detection and differentiation of RNA from dengue, Chikungunya and Zika, was provided by CDC as part of the zika emergency preparedness effort. Of the nine samples tested, eight (88.88%) were found to be positive for chikungunya virus nucleic acid but negative for dengue and Zika virus nucleic acids. The median age of the affected sampled patients was 40 years, and males appear to be more affected (66.6% of sampled patients). The laboratory investigation confirmed that the outbreak was caused by chikungunya virus. Even though further molecular characterization of the positive isolates will provide more information as to the circulating genotypes and elucidate the origin of the outbreak virus, it is also possible to assume that the outbreak was an extension of the outbreak in neighboring countries in Kenya and, therefore, warrants that cross-border integration efforts to control chikungunya should be implemented by the concerned countries.

Background Yellow fever (YF) is a viral hemorrhagic fever, endemic in the tropical forests of Africa and Central and South America. The disease is transmitted by mosquitoes infected with the yellow fever virus (YFV). Ethiopia was affected by the largest YF outbreak since the vaccination era during 1960–1962. The recent YF outbreak occurred in 2013 in Southern part of the country. The current survey of was carried out to determine the YF seroprevalence so as to make recommendations from YF prevention and control in Ethiopia. Methodology A multistage cluster design was utilized. Consequently, the country was divided into 5 ecological zones and two sampling towns were picked per zone randomly. A total of 1643 serum samples were collected from human participants. The serum samples were tested for IgG antibody against YFV using ELISA. Any serum sample testing positive by ELISA was confirmed by plaque reduction neutralization test (PRNT). In addition, differential testing was performed for other flaviviruses, namely dengue, Zika and West Nile viruses. Result Of the total samples tested, 10 (0.61%) were confirmed to be IgG positive against YFV and confirmed with PRNT. Nine (0.5%) samples were antibody positive for dengue virus, 15(0.9%) forWest Nile virus and 7 (0.4%) for Zika virus by PRNT. Three out of the five ecological zones namely zones 1, 3 and 5 showed low levels (

The Ethiopian Rift Valley Lakes host populations of edible fish species including Oreochromis niloticus, Labeobarbus intermedius and Clarias gariepinus, which are harvested also in other tropical countries. We investigated the occurrence of six heavy metals in tissues of these fish species as well as in the waters of Lake Koka and Lake Awassa. Both lakes are affected by industrial effluents in their catchments, making them ideal study sites. Mercury concentrations were very low in the water samples, but concentrations in the fish samples were relatively high, suggesting a particularly high bioaccumulation tendency as compared with the other investigated metals. Mercury was preferentially accumulated in the fish liver or muscle. It was the only metal with species-specific accumulation with highest levels found in the predatory species L. intermedius. Lower mercury concentrations in O. niloticus could be attributed to the lower trophic level, whereas mercury values in the predatory C. gariepinus were unexpectedly low. This probably relates to the high growth rate of this species resulting in biodilution of mercury. Accumulation of lead, selenium, chromium, arsenic and cadmium did not differ between species, indicating that these elements are not biomagnified in the food chain. Values of cadmium, selenium and arsenic were highest in fish livers, while lead and chromium levels were highest in the gills, which could be related to the uptake pathway. A significant impact of the industrial discharges on the occurrence of metals in the lakes could not be detected, and the respective concentrations in fish do not pose a public health hazard.