# Mathematical Models Should Be Used to Help The Prevention of Infectious Disease Transmission

Infectious disease transmission has been widely discussed among nations over the last two decades. All countries across the globe are worried about this issue since the communicable diseases are borderless transmitted and there is a significant increase of the mortality rate caused by the escalating number of people who are suffered from particular transmissible diseases like HIV-AIDS and Tuberculosis. Epidemiologists have struggled for describing the transmission pattern of the infectious diseases and how to forecast the possible approaches that can be implemented in the future. To tackle this problematic situation, mathematical models have been developed as a viable remedy for describing the phenomena of how the infectious diseases are spread (Hethcote, 2000) and feasible predictions of further development of the transmissible disease phenomena (Koopman, 2002). According to Schichl (2010) mathematical modeling is a range of process to determine a mathematical formula which can describe the real world problems for providing knowledge, solutions, and suggestions that can be applied in further applicative fields. The models mathematically represent abstractions of natural phenomena that are difficult to be explained and understood (Tedeschi, 2006), like the communicable disease transmission. However, mathematical models have been criticized regarding their functionality in terms of providing abstractions of the spread of transmissible diseases, the accuracy of projections and conceptual frameworks for strategic-decision makers. This essay will discuss these three main issues to illustrate that mathematical models are incredibly useful tools for helping communicable disease prevention.

The abstraction of reality

In terms of infectious disease phenomena abstractions, Ferguson, Keeling, Edmunds, Gani, Grenfell, Anderson, and Leach (2003) believe that it is still questionable whether a complex model might provide correct assumptions of the real disease problems. These scholars think that this is because most complex models input lots of parameters to explain certain phenomena. It has also been claimed that the outputs of mathematical models never provide any exact estimations of reality (Keeling, 2005). Nevertheless, Hollingsworth (2009) asserts that mathematical models for epidemiology present several cognitive outcomes like the numbers of thresholds, primary reproduction, and substitution. He highlights that the more complicated an epidemiological model the more it can provide a better abstraction of the reality. As Hethcote (2000) and de Castro Medeiros, Castilho, Braga, de Souza, Regis, and Monteiro (2011) state, this is because the epidemiological models allow us to make a combination of complicated data. As a consequence, these can be used to make identification of how the communicable diseases can be transmitted (Hethcote, 2000). It has also been shown that mathematical models in epidemiology offer certain basic abstractions such as the period of generations, the levels of epidemic growth and the principal and effective numbers of prolificacy which take an essential part in describing the characteristics of communicable diseases (Hollingsworth, 2009). Therefore, it seems clear that complex mathematical models are beneficial tools in providing acceptable assumptions which reflect how the communicable diseases are transmitted.

Another issue is been raised by Klein, Laxminarayan, Smith, and Gilligan (2007), who argue that there are only fewer numbers of mathematical models that have explained clearly about the logic characters of individual cases; therefore, unfortunately, it has a negative impact on describing the clustering cases. Nonetheless, Punyacharoensin, Edmunds, De Angelis, and White (2011) state that there has been a significant increase in the numbers of mathematical models that are used in explaining the transmission of infectious diseases recently. Take mathematical models of the HIV transmission, for example, there is a considerable increase in the numbers of the models that have been used to explain the HIV transmission and its surveillances over the last 25-year period (Punyacharoensin et al., 2011). This is because those models link the reality with observations which can be beneficial in enriching the reliability of the real word problems (Byl, 2003; Hollingsworth, 2009; de Castro Medeiros et al., 2011). Research which has been carried out by de Castro Medeiros et al. (2011), for example, has contributed in providing a better explanation of how dengue fever is transmitted. Moreover, the communicable disease models can show an abstraction of the real phenomena which serve information about the hosts’ position and the possible combination which can be created between the history of endemic diseases and numeric illustrations about the transmission stages (Riley, 2007). Thus, despite what Klein et al. (2007) claim, it can clearly be seen that modeling mathematics for epidemiology has been used popularly in describing the transmission of infectious diseases recently.

Prediction accuracy

It has been argued that the precision of such predictions, which is provided by a mathematical model for explaining the transmission of viruses, is doubtful (Colizza, Barrat, Barthelemy, Valleron, & Vespignani, 2007). These scholars think that this is because the models include the numbers of irrelevant assumptions like the character of viruses. They also believe that there is no any consideration about how the viruses spread with varieties frequency in remote areas. However, Kitching, Hutber, and Thrusfield (2005) criticize this idea by asserting that in general, mathematical models in epidemiology can predict how long an epidemic can last, the range of its geographic and the prevalence numbers. They highlight this as the most ultimate reason why mathematical models of epidemiology are built and used. They also prove that in San Francisco, for instance, the resistant incidence and its prevalence have been predicted by epidemiological models. Hethcote (2000) emphasizes what Kitching et al. (2005) have indicated by stating that as beneficial tools during the experiment, mathematical models have power to test and build theories, solve distinctive problems, give quantitative assessments on hypothesis, determine sensitive alteration of values of estimators and conjecture important data estimators. Moreover, it has also been demonstrated that mathematical models for epidemiology offer probability to deepen analysis about the disease problems (de Castro Medeiros et al., 2011). Therefore, due to these reasons, it is totally true that modeling mathematics for epidemiology offers accurate predictions about the phenomena of infectious diseases.

Furthermore, Colizza, Barrat, Barthélemy, and Vespignani (2006) assume that how models can predict effectively is still a key problem related to epidemiological modeling of global phenomena. One probably reason is because mathematical models offer probabilistic calculations which mean the accuracy of such predictions are being decayed as the projections are made (Keeling, 2005). Nevertheless, Logan (2005) censures these scholars for this claim. He demonstrates that as what physical models can predict the mechanical structure, biological models also can forecast how living structures run. Epidemiological models can be used to forecast the general phenomena in the future and provide estimations of the projection uncertainty (Hethcote, 2000; Blower, Schwartz, & Mills, 2003). As what Chubb and Jacobsen (2010) explain, mathematical models help epidemiological research in making evaluations of the variables’ significance, predicting the possible outputs of a project and providing extra data clarification. Additionally, Ferguson et al. (2003) demonstrate that the power of forecasting the possible process that occur individually and interaction within the population is one of the undeniable advantages of the use of mathematical models. There is also an absolute possibility to predict the geographical area of disease infection by using a probabilistic model that has been created for describing how the diseases trans-missed globally (Hufnagel, Brockmann & Geisel, 2004). Although the effectiveness of the prediction provided by epidemiological models is still problematic but it seems incredibly true that without using the models we could not forecast the future possible trends of communicable disease transmission.

Conceptual frameworks for the policy makers

Concerning to public health policies, it has been claimed that even though epidemiological models have contributed to solve global issues related to infectious diseases, they still lack benefits for the formulation and surveillance programs (Woolhouse, 2003). Klein et al. (2007) believe that one reason is because the models generalize the infection pattern by providing a similar assumption of how diseases are infected todays with the previously. Nonetheless, Blower et al. (2003) condemn these scholars for claiming as so. They assert that modeling mathematics offer strong recommendations for the policy makers to formulate public health strategies. This is due to the models warning them about the effectiveness of insulations that might reduce the infection burdens and the efficiency of vaccines that should be delivered (Bozzette, Boer, Bhatnagar, Brower, Keeler, Morton, & Stoto, 2003). Moreover, Keeling (2005) has demonstrated that the epidemiological models provide a help to inform the policy makers about the best way to maximize the limitation of resources. The models also suggest what essential data which are needed in other surveys in the future (Hethcote, 2000). Additionally, it has been demonstrated that modeling mathematics is incredibly capable to provide a brief evaluation of the prevention policies for controlling the chronic diseases like cancer and diabetes (Dasbach, Elbasha, & Insinga, 2006). Thus, it is clear that in relation to the public health policies, modeling mathematics plays an important part in supplying information which is unexpectedly needed by the decision makers.

Additionally, Klein et al. (2007) say that there has been a failure in constructing policies to overcome the issue of infectious diseases transmission using mathematical models’ analysis. These scholars assume that there was no consideration in the analysis about the possible cost that might be needed in the future. They claim what happen in 1990 as an indication that mathematical models has failed to provide guidance for the policy makers to construct strategies for combating the HIV spread. However, it has been demonstrated that epidemiological models contribute as a useful tool for designing and planning further observations related to epidemiology (Keeling & Rohani, 2008; Bozzette et al., 2003; Punyacharoensin et al., 2011). As Hethcote (2000) states, epidemiological models are a useful tool for making comparisons, evaluation and optimization of detection diversities, surveillance and prevention programs, and recovery actions. For instance, Donaldson and Alexandersen (2002) have demonstrated that a mathematical model plays an essential part in constructing the emergency strategies that should be taken once foot-mouth-disease (FMD) is transmitted. Moreover, Dasbach et al. (2006) assert that the use of the epidemiological models in the cervical cancer cases, for example, has been applied in the development and evaluation of the screening strategies for the cancer issues. Therefore, there are no reasons to question about the success of mathematical models in supporting the formulation strategies related to preventive actions of the infectious diseases.

Conclusion

In conclusion, this essay has outlined the importance of mathematical models for overcoming the infectious disease transmission issues through covering three main focus areas, namely, abstraction of the reality, prediction accuracy, and conceptual frameworks for policy makers. Even though there is same critic related to modeling mathematics due to the complexity and the clarity of the assumptions which are covered by the models, the uncertainty of their accuracy in predicting the future trends, and their contributions in giving direction for the policy makers to formulate the preventive actions and strategies, it has been shown that there is a meteoric increase in the numbers of epidemiological models which are used to explain how the communicable diseases are spread. One reason is because the models are able to provide acceptable assumptions in reflecting the transmission of the infectious diseases. Additionally, they also offer accurate projections by drawing assessments quantitatively on hypothesis and determining sensitive alteration of values of estimators. Furthermore, the models enable the decision makers to confidently design preventive strategies by supplying crucial information like the effectiveness of insulations and the efficiency of vaccines that should be delivered. Therefore, by using mathematical models we would be able to have better illustrations about how the diseases are transmitted and anticipate the further actions for preventing the communicable disease transmission. Without modeling mathematics for epidemiology, it may be impossible to combat the infectious diseases and numbers of people who are suffered from the infectious diseases will continue to increase.

References

Blower, S., Schwartz, E., & Mills, J. (2003). Forecasting the future of HIV epidemics: The impact of antiretroviral therapies and imperfect vaccines. AIDS Reviews, 5(2), 113-125. Retrieved from http://aidsreviews.com/files/2003_05_2_113-125.pdf

Bozzette, S. A., Boer, R., Bhatnagar, V., Brower, J. L., Keeler, E. B., Morton, S. C., & Stoto, M. A. (2003). A model for a smallpox-vaccination policy. New England Journal of Medicine, 348(5), 416-425.

Byl, J. (2003). Mathematical models and reality. Paper presented at Proceedings of the 2003 Conference of the Association for Christians in the Mathematical Sciences. Retrieved from www.csc.twu.ca/byl/modelstest.doc

Chubb, M. C., & Jacobsen, K. H. (2010). Mathematical modeling and the epidemiological research process. European Journal of Epidemiology, 25(1), 13-19.

Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2006). The modeling of global epidemics: Stochastic dynamics and predictability. Bulletin of Mathematical Biology, 68(8), 1893-1921. Retrieved from http://www.epicx-lab.com/uploads/9/6/9/4/9694133/bmb68.pdf

Colizza, V., Barrat, A., Barthelemy, M., Valleron, A.-J., & Vespignani, A. (2007). Modeling the worldwide spread of pandemic influenza: Baseline case and containment interventions. PLoS Medicine, 4(1), e13. Retrieved from http://www.plosmedicine.org/article/info%3Adoi%2F10.1371%2Fjournal.pmed.0040013

Dasbach, E. J., Elbasha, E. H., & Insinga, R. P. (2006). Mathematical models for predicting the epidemiologic and economic impact of vaccination against human papillomavirus infection and disease. Epidemiologic Reviews, 28(1), 88-100. Retrieved from http://epirev.oxfordjournals.org/content/28/1/88.full

de Castro Medeiros, L. C., Castilho, C. A. R., Braga, C., de Souza, W. V., Regis, L., & Monteiro, A. M. V. (2011). Modeling the dynamic transmission of dengue fever: Investigating disease persistence. PLOS Neglected Tropical Diseases, 5(1), e942. Retrieved from http://www.plosntds.org/article/info%3Adoi%2F10.1371%2Fjournal.pntd.0000942

Donaldson, A. I., & Alexandersen, S. (2002). Predicting the spread of foot and mouth disease by airborne virus. Revue Scientifique et Technique-Office International des épizooties, 21(3), 569-578. Retrieved from www.researchgate.net/publication/15160049_Modelling_the_spread_of_foot-and-mouth_disease_virus/file/60b7d519138c080647.pdf

Ferguson, N. M., Keeling, M. J., Edmunds, W. J., Gani, R., Grenfell, B. T., Anderson, R. M., & Leach, S. (2003). Planning for smallpox outbreaks. Nature, 425(6959), 681-685.

Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599-653. Retrieved from http://web.abo.fi/fak/mnf/mate/kurser/dynsyst/2009/uke44/hethcote2000.pdf

Hollingsworth, T. D. (2009). Controlling infectious disease outbreaks: Lessons from mathematical modelling. Journal of Public Health Policy, 30(3), 328-341.

Hufnagel, L., Brockmann, D., & Geisel, T. (2004). Forecast and control of epidemics in a globalized world. Proceedings of the National Academy of Sciences of the United States of America, 101(42), 15124-15129. Retrieved from http://www.pnas.org/content/101/42/15124.long

Keeling, M. J. (2005). Models of foot-and-mouth disease. Proceedings of the Royal Society Biological Sciences, 272(1569), 1195-1202. Retrieved from http://rspb.royalsocietypublishing.org/content/272/1569/1195.full

Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Retrieved from http://vserver1.cscs.lsa.umich.edu/~rohani/Publications/Bookfiles/pourbouhloul.pdf

Kitching, R. P., Hutber, A., & Thrusfield, M. (2005). A review of foot-and-mouth disease with special consideration for the clinical and epidemiological factors relevant to predictive modelling of the disease. The Veterinary Journal, 169(2), 197-209.

Klein, E., Laxminarayan, R., Smith, D. L., & Gilligan, C. A. (2007). Economic incentives and mathematical models of disease. Environment and Development Economics, 12(5), 707.

Logan, J. D. (2005). Mathematical models in Biology. The American Mathematical Monthly, 112(9), 847-850.

Punyacharoensin, N., Edmunds, W. J., De Angelis, D., & White, R. G. (2011). Mathematical models for the study of HIV spread and control amongst men who have sex with men. European Journal of Epidemiology, 26(9), 695-709. doi:10.1007/s10654-011-9614-1

Riley, S. (2007). Large-scale spatial-transmission models of infectious disease. Science, 316(5829), 1298-1301.

Tedeschi, L. O. (2006). Assessment of the adequacy of mathematical models. Agricultural Systems, 89(2), 225-247.

Woolhouse, M. (2003). Foot‐and‐mouth disease in the UK: What should we do next time? Journal of Applied Microbiology, 94(s1), 126-130. Retrieved from http://onlinelibrary.wiley.com/doi/10.1046/j.1365-2672.94.s1.15.x/full

# 2 lebih baik dari pada 1

Malam ini kembali Ia berkawan gelap, menyepi di sela-sela tumpukan lelah itu

Ditatapinya lampion-lampion yang benderang cahayanya dari seberang sana

Sisir demi sisir alam bawah sadar Ia telusuri, hingga tak berkutik walau dengan lalat sekalipun

Sang bayu pun enggan menyapanya, seolah memberi ruang buat Ia semakin berkreasi

Sesekali kedipan mata terlihat dari wajah belia itu, pertanda Ia masih bertutur kepada dunia

Sekalipun raga seolah tak bernyawa, sekalipun jiwa yang mendiami tubuh itu sedang asyik bertamasya ke negeri 7 dondeng, Ia masih tetap berkedip, bernapas

Ia masih mampu merasakan sentuhan lembut angin yang berhembus di sekitarnya

Ia pun masih dapat mendengar hilir mudik angkutan umum di jalanan

Matanya masih mampu melihat remang-remang cahaya dari gedung di sebelah sana

Orang akan mengira, Ia bisu, tuli dan buta

Tapi mereka salah Matanya masih dengan daya akomodasi yang sempurna

Telinga nya masih juga dalam performa yang luar biasa

Bahkan mulutnya masih dapat berkata-kata

Orang-orang itu terus menggodanya agar respon Ia berikan

Umpan terus mereka berikan agar Ia terbangun dari tidurnya itu

Tapi Ia masih asyik berkelana hingga ke kolong-kolong jembatan

Tubuhnya memang masih di situ tapi jiwa yang mendiaminya sedang tidak ada di tempat

Jiwa itu hanya ingin membiarkan raga saudaranya itu ruang

Ruang untuk menikmati dirinya sendiri

Ruang yang buat Raga kelihatan seperti tak berfungsi lagi

Jiwa itu juga sedang menari-nari menikmati kebebasannya

Jiwa yang selama ini tertaut dengan raga yang egois

Mereka berdua rupanya tahu bahwa tanpa kesatuan mereka, insan muda yang adalah perkawinan jiwa dan raga itu tidak mungkin ada

Kini, mereka kembali menyatu Aura hangat kembali terpancar dari roman belia itu

Orang-orang itu kembali ke peraduan mereka masing-masing

Sambil mengulum senyum puas Ia sudah kembali

HIDUP

Melbourne, 23 Januari 2015

# 2014 in review

The WordPress.com stats helper monkeys prepared a 2014 annual report for this blog.

Here’s an excerpt:

A New York City subway train holds 1,200 people. This blog was viewed about 5,000 times in 2014. If it were a NYC subway train, it would take about 4 trips to carry that many people.

# Sempurna

Seutas mimpi sudah Ia genggam.
Matahari yang dulunya garang sudah Ia taklukan.
Samudera yang kata mereka tak berujung itu pun sudah Ia arungi.
Bahkan, bukit tinggi di seberang sana yang tersohor ke seluruh penjuru karena tingginya yang menggapai awan itu sudah pula Ia tanjaki.
Ah, memang tak ada kepuasaan seindah menggapai asa.
Terbang melintasi angan-angan yang dulu nampaknya mustahil.

Tumpukan kisah memang telah Ia goreskan.
Susah senang, pahit manis, menu utama baginya.
Namun, mereka yang di luar sana selalu duduk dalam tanya.
Tanya??
Ah, sepertinya bukan.
Mereka bukan bertanya.
Mereka bicara.
Berceloteh.
“Dia hanya beruntung”
Beruntung??
Apa??

Hey, kalian hanya melihat casingnya saja.
Tidakkah kalian tahu, bahwa untuk sampai di puncak bukit tinggi di seberang itu Ia harus berbeban keringat dan lelah pendakian berhari-hari?
Bukankah kalian tahu kalau Ia dulu selalu kalian ejeki karena kulit gosongnya? Itu karena, matahari yang berhasil Ia taklukan itu.
Kalian juga tahu kan, kalau untuk sampai di ujung samudera luas itu Ia harus mengayuh sampan tua miliknya itu siang malam.
Ah, kalian ini.
Sudah tahu tapi seolah bodoh,
Ia masih seperti yang dulu.
Masih indah dalam kesederhaannya.
Yang Ia ingin tunjukkan hanyalah hidup ini bisa SEMPURNA kalau kalian tahu betul apa yang kalian inginkan.

Melbourne,

20 Januari 2015

# Setiap lagu punya memori tersendiri, setidaknya buat saya. Kamu?

Sepertinya ini sudah sering menimpa saya. Kebiasaan yang seperti kebetulan bagi saya. Iya. Hampir beberapa lagu yang sering saya dengar entah karena suka ataupun hanya sambil lalu, ternyata mewakili sebuah moment tertentu. Semula saya memang acuh saja soal hal aneh yang belakangan baru saya sadari ini. Lagu ‘Angels brought me here’ miliknya Guy Sebastian, misalnya. Lagu yang akhirnya jadi salah satu dari lagu favorit saya itu selalu melekat dengan sebuah memori perjalanan dari Jakarta ke Bandung dalam sebuah bus travel. Ya, setiap kali mendengar lagu itu, ingatan saya selalu dibawa ke momen memorable itu. Waktu itu tahun 2006 saat saya bersama 7 putra/i daerah terpilih dari NTT berkunjung ke Bandung dalam sebuah misi penting mewakili NTT dalam sebuah event nasional; Temu Budaya Se-Indonesia yang berlangsung selama hampir seminggu di Aula Taman Budaya Bandung, Jawa Barat. Ah, lagu itu betul-betul berbekas dengan memori perjalanan dalam bus. Entah kenapa, setiap lagu itu saya dengar, saya seoalah sedang dalam bus, persis seperti moment saat itu.

Bukan baru sekali saya mengalami sindrom aneh ini. Saat ini misalnya. ‘Thingking Out Loud’ yang merupakan hits dari Ed Sheeran agaknya akan membawa saya dengan sebuah memori liburan kemarin. Sudah hampir seminggu ini lagu itu ngehits di pendengaran saya. Lagu itu selalu membawa saya ke memori hang out bersama-sama sahabat-sahabat saya yang super duper gokil. Lagu itu lebih tepatnya mengingatkan saya akan kehebohan kami saat ber-cover lagu di depan webcam. WHAT??? LOL. Padahal, kalau mau dikaitkan dengan isi dan pesan lagu itu, sungguh keluar jalur. Aneh tapi nyata. Setipa kali saya memutar lagu itu, kenaangan itu yang dengan cepat kilat menyambar benak saya.

Lagu-lagu boyband kenamaan asal Irlandia, Westlife pun punya memori tersendiri. Lagu-lagu yang hits saat masa-masa SMP kemarin selalu berhasil meringkus saya buat tenggelam dalam nostalgia masa-masa SMP. Masa-masa ketika numang bemo pulang sekolah dan mendengar lagu-lagu westlife diputar dengan volume yang cukup tinggi dalam angkot. Sungguh luar biasa. Seperti dua lagu di atas, setiap mendengar lagu-lagu Westlife saya seolah dirasuki dengan memori tahun 2000 itu. Oalahhhh…. jadul sekali yah!!!

Masih banyak lagi lagu lain yang tidak dapat saya sebutkan satu per satu di coretan kali ini. Maklum saja, tulisan ini saya buat saat sedang di ruang tunggu bandara Ngurah Rai, Denpasar. menunggu boarding ke Melbourne. Dengan goresan ini saya akhirnya sadar kalau lagu-lagu yang sekarang ramai bertengger di playlist saya di laptop ataupun di handphone ini rupanya mewakili moment-moment yang tak terlupakan selama ini. Bersyukur saya punya list-list ini. Setidaknya ssat saya rindu ingin kembali ke suatu memori, saya cukup memutar ulang lagu-lagu itu tanpa harus stress mencari mesin waktu miiliknya Doraemon atau kalung pemutar waktu milik Harmoine yang bisa dengan mudah mengahantarkan kita ke moment masa lalu. Aha!!!!! COOL !!!  Memang ini kedengaran aneh, tapi ini nyata. Ini pengalaman saya. Kamu? Adakah anyone out there yang juga mengalami hal yang sama?

Salam,

15 Januari 2015

Gate 7 Bandara Internasional Ngurah Rai, Denpasar.

# Mendung Tak Selamanya Hujan: Sepenggal kisah liburan di dusun kecil; Atambua.

Ini kisah saya selama summer break sebulan kemarin. Menghabiskan waktu dengan sanak keluarga dan jam tidur yang semakin panjang jadi agenda utama saya selama liburan di kampung halaman. Betapa tidak, setelah dua semester menghabiskan waktu di perpustakaan kampus, nampaknya waktu luang selama kurang lebih satu bulan ini tidak mau saya sia-siakan. Penat akibat beban belajar yang luar biasa hebat ditambah deadline tugas yang sangat rapat, sudah cukup membuat otak ini hampir meledak. Beruntung saya masih mendapat kesempatan off; menikmati waktu bebas yang sebentar lagi akan berakhir. Ya, sisa sehari lagi dan saya akan kembali ke rutinitas yang belakangan saya lakoni. Duduk berjam-jam depan laptop sambil berkawankan setumpuk bacaan kembali terbayang samar dalam pikiran saya saat ini. Hufffttt, tenang..tenang!! Kurang lebih 10 bulan lagi perjuangan studi saya selesai. Yang saya butuhkan adalah semangat baru buat kembali ke kota itu, kota yang dikenal dengan empat musim sehari itu, kota yang terkenal karena sebutan ‘The most liveable city in he world“; Melbourne.

Ah, saya sudah tak sabar lagi buat segera kembali. Mungkin ini berkat sebulan liburan di rumah bapa mama. Energi dan semangat saya seolah baru selesai diisi kembali. Perasaan saya ringan sekali dan beban-beban yang tertumpuk sebelum liburan kemarin seakan meleleh karena sengatan panas bara semangat bertemu bapa mama dan kawan-kawan lama kemarin. Syukur Puji Tuhan, saya masih dikasih kesempatan emas ini. Kesempatan luar biasa yang tentu sudah saya manfaatakan dengan baik. Tentu, kalian bertanya-tanya, apa istimewanya pulang ke dusun kecil di daerah perbatasan RI – RDTL itu. Ops, jangan salah! Atambua, kota kecil itu menyimpan banyak memori. Biar saya tidak lahir dan besar di kota itu, tetapi saya sudah mengukir banyak kenangan semenjak 1999, saat eksodus dari Timor-Timor. Saya menyelesaikan sekolah dasar hingga menengah atas di kota kecil itu. Sehingga jangan heran, mau sampe mana saja saya pergi saya akan selalu kembali ke Atambua. Di sana, saya punya sahabat-sahabat yang luar biasa. Kenapa? Buktinya, setelah sekian lama tidak bertemu, mungkin sekitar 8 tahunan, kami masih sangat hangat satu sama lain. Waktu memang benar sudah memisahkan kami begitu lama, akan tetapi saat reuni kecil-kecilan kemarin kami masih saja tenggelam dalam candaan jaman SMA lalu. Beberapa hal saja yang sedikit berbeda dari kami. Misalnya, sekarang semua sudah sibuk dengan kerjaan masing-masing. Adapula yang sudah dalam persiapan menuju rumah tangga baru. Wah, amazing!! Waktu berputar sangat cepat. Siapa sangka, mereka-mereka yang sekarang sudah saya anggap seperti saudara sendiri ini, dulu adalah orang-orang asing yang jarang saling sapa. Siapa sangka, pribadi-pribadi yang kini sangat dekat ini dahulu adalah orang-orang biasa yang tidak lebih dari “classmates”. Sekali lagi, waktu memang luar biasa. Ia setia menemani kami berproses hingga menjadi sedekat ini. Ya, dekat sekali. Yang saya ingat dari liburan di rumah kemarin adalah sesi gila-gilaan kami berkaraoke di depan webcam laptop salah satu sahabat baik saya. Bahkan ide gila itu kami teruskan dengan merekam dengan sebuah kamera DSLR keren milik saya dan salah seorabg kawan baik saya lainnya. Wah…wah!! Kami sampe lupa umur kami, pemirsa!! LOL… But, anyway, hidup itu sekali saja, bukan? Masa muda juga hanya sekali saja, tidak bisa diputar ulang. Karena itulah kami sepakat buat menikmati setiap momen kebersamaan kami kemarin. Saya pribadi betul-betul larut dengan reuni yang hampir kami lakukan setiap hari. Berkumpul hingga masak-masak adalah dua dari agenda kami setiap hari. Sampai-sampai saya dibut galau sekarang. Ya, kapan lagi bisa kumpul lagi dengan momen sperti itu? Who knows?

Well…. summer break kemarin betul-betul menjadi obat pelipur lara yang luar biasa ampuh. Masalah hati yang sempat membelenggu saya kemarin kini sudah bisa saya tinggalkan dan tanggalkan sebagai sejarah tahun kemarin. “Life must go on, no matter how hard it was. All you need is just keep climbing because life is a climb and the view is great.” Saat ini, hati sudah terasa sangat damai. Otak sudah terasa ringan dan yang pasti semangat untuk menaklukan 2 semester lagi semakin membara. Terima kasih BAPA buat rahmat selama liburan. Terima kasih buat semua anugerah yang selalu KAU limpahkan hingga hari ini. Syukur berlimpah selalu saya naikkan kehadirat MU, selalu dan selalu.

Selamat berkarya di tahun 2015 buat basodara semua. Be blessed ^_^

Kupang, 14 January 2015